https://wiki.cs.hse.ru/api.php?action=feedcontributions&feedformat=atom&user=Popova.svetlana Wiki - Факультет компьютерных наук - Вклад [ru] 2026-09-20T21:52:24Z Вклад MediaWiki 1.43.9 https://wiki.cs.hse.ru/index.php?title=Tssp-2023-24&diff=79866 Tssp-2023-24 2023-09-17T11:25:34Z <p>Popova.svetlana: /* Log Book */</p> <hr /> <div>== General course info ==<br /> <br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Practice and problem solving: Boris Demeshev, Friday 16:20-17:40 Moscow time, [https://zoom.us/j/8126338383 zoom]<br /> <br /> Class teacher: [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova<br /> <br /> <br /> <br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I: Stochastic Processes ====<br /> <br /> [https://www.youtube.com/playlist?list=PLnIS95ct9auXMX-4-ESGZvigU1w6kexw0 Practice playlist]<br /> <br /> '''Week 1. 2023-09-04'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2023-24/raw/main/lectures/TSSP_23_m1_l1%202.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis, [https://github.com/bdemeshev/tssp_2023-24/raw/main/classes/HSE_sem1.pdf pdf by Maria] <br /> <br /> Practice. MGF and first step analysis, [https://github.com/bdemeshev/tssp_2023-24/raw/main/practice/practice-01.pdf pdf]<br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2023-09-11'''<br /> <br /> Lecture. Markov chains, classification of states, [https://github.com/bdemeshev/tssp_2023-24/raw/main/lectures/TSSP_23_m1_l2.pdf pdf]<br /> <br /> '''Week 3. 2023-09-16'''<br /> <br /> Lecture. Convergence. [https://github.com/bdemeshev/tssp_2023-24/raw/main/lectures/TSSP_23_m1_l3.pdf pdf]<br /> <br /> ==Sources of Wisdom==<br /> <br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [https://github.com/bdemeshev/stochastic_pro/raw/main/stochastic_pro.pdf a lot of problems...] (under construction)<br /> * [https://t.me/spts2023 TG chat 2023-24]<br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.7/stat-cookbook.pdf Statistics cookbook]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22], [http://wiki.cs.hse.ru/Tssp-2022-23 Wiki 2022-23]<br /> <br /> <br /> === MC + MCMC ===<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=77198 Tssp-2022-23 2023-03-24T08:31:35Z <p>Popova.svetlana: /* Sources */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/749646288 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> Нажми &quot;развернуть&quot;, чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣<br /> &lt;div class=&quot;mw-collapsible mw-collapsed&quot; style=&quot;width:1000px; overflow: hidden;&quot;&gt;<br /> <br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> &lt;/div&gt; <br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01a-white-noise-stationarity.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01b-white-noise-stationarity.pdf online-B]<br /> <br /> '''Week 2. 2023-01-21'''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-B]<br /> <br /> '''Week 3. 2023-01-28 '''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: recurrence equations and AR(1), Yule-Wolker equations for PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-B]<br /> <br /> '''Week 4. 2023-02-04 '''<br /> <br /> Lecture. Forecasting with ARMA, ADF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Forecasting with AR, MA(infty) solutions [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w04a-ma-infinity-solution.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w04b-ma-infinity-solution.pdf online-B]<br /> <br /> '''Week 5. 2023-02-11 '''<br /> <br /> Lecture. Non-Stationary Time Series, Holt-Winter's exponential smoothing [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l5_done.pdf pdf]<br /> <br /> Class. ACF, PACF for ARMA(1,1), AR(2) [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w05a-arma11-pacf-interpretation online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w05b-arma11-pacf-interpretation.pdf online-B]<br /> <br /> '''Week 6. 2023-02-18 '''<br /> <br /> Lecture. ETS-model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_lecture-2023-02-18_ets-problems.pdf pdf]<br /> <br /> Class. ETS(AAA) [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w06a-ets-aaa.pdf, online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w06b-ets-aaa.pdf online-B]<br /> <br /> '''Week 7. 2023-02-25 '''<br /> <br /> Lecture. GARCH [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l7.pdf pdf]<br /> <br /> '''Week 8. 2023-03-04'''<br /> <br /> Lecture. UOL - point, interval estimators [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l8.pdf pdf]<br /> <br /> '''Week 9. 2023-03-11 '''<br /> <br /> Lecture. UOL - Fisher info [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l9.pdf pdf]<br /> <br /> Class. Fisher info<br /> <br /> '''Week 10. 2023-03-18 '''<br /> <br /> Lecture. UOL<br /> <br /> '''Week 11. 2023-03-25 '''<br /> <br /> Midterm<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [https://github.com/bdemeshev/tssp_2022-23/tree/main/online_classes handwritten class notes]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео очных семинаров 2022-23], [https://disk.yandex.ru/d/ceN8JNGEE1GE5Q видео Zoom семинаров 2022-23]<br /> * [https://disk.yandex.com.ge/d/o_QCTfVi_hLQSA видео консультаций 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=77165 Tssp-2022-23 2023-03-22T17:53:48Z <p>Popova.svetlana: /* Semester II */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/749646288 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> Нажми &quot;развернуть&quot;, чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣<br /> &lt;div class=&quot;mw-collapsible mw-collapsed&quot; style=&quot;width:1000px; overflow: hidden;&quot;&gt;<br /> <br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> &lt;/div&gt; <br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01a-white-noise-stationarity.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01b-white-noise-stationarity.pdf online-B]<br /> <br /> '''Week 2. 2023-01-21'''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-B]<br /> <br /> '''Week 3. 2023-01-28 '''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: recurrence equations and AR(1), Yule-Wolker equations for PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-B]<br /> <br /> '''Week 4. 2023-02-04 '''<br /> <br /> Lecture. Forecasting with ARMA, ADF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Forecasting with AR, MA(infty) solutions [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w04a-ma-infinity-solution.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w04b-ma-infinity-solution.pdf online-B]<br /> <br /> '''Week 5. 2023-02-11 '''<br /> <br /> Lecture. Non-Stationary Time Series, Holt-Winter's exponential smoothing [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l5_done.pdf pdf]<br /> <br /> Class. ACF, PACF for ARMA(1,1), AR(2) [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w05a-arma11-pacf-interpretation online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w05b-arma11-pacf-interpretation.pdf online-B]<br /> <br /> '''Week 6. 2023-02-18 '''<br /> <br /> Lecture. ETS-model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_lecture-2023-02-18_ets-problems.pdf pdf]<br /> <br /> Class. ETS(AAA) [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w06a-ets-aaa.pdf, online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w06b-ets-aaa.pdf online-B]<br /> <br /> '''Week 7. 2023-02-25 '''<br /> <br /> Lecture. GARCH [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l7.pdf pdf]<br /> <br /> '''Week 8. 2023-03-04'''<br /> <br /> Lecture. UOL - point, interval estimators [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l8.pdf pdf]<br /> <br /> '''Week 9. 2023-03-11 '''<br /> <br /> Lecture. UOL - Fisher info [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l9.pdf pdf]<br /> <br /> Class. Fisher info<br /> <br /> '''Week 10. 2023-03-18 '''<br /> <br /> Lecture. UOL<br /> <br /> '''Week 11. 2023-03-25 '''<br /> <br /> Midterm<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [https://github.com/bdemeshev/tssp_2022-23/tree/main/online_classes handwritten class notes]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=77164 Tssp-2022-23 2023-03-22T17:53:22Z <p>Popova.svetlana: /* Semester II */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/749646288 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> Нажми &quot;развернуть&quot;, чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣<br /> &lt;div class=&quot;mw-collapsible mw-collapsed&quot; style=&quot;width:1000px; overflow: hidden;&quot;&gt;<br /> <br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> &lt;/div&gt; <br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01a-white-noise-stationarity.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01b-white-noise-stationarity.pdf online-B]<br /> <br /> '''Week 2. 2023-01-21'''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-B]<br /> <br /> '''Week 3. 2023-01-28 '''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: recurrence equations and AR(1), Yule-Wolker equations for PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-B]<br /> <br /> '''Week 4. 2023-02-04 '''<br /> <br /> Lecture. Forecasting with ARMA, ADF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Forecasting with AR, MA(infty) solutions [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w04a-ma-infinity-solution.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w04b-ma-infinity-solution.pdf online-B]<br /> <br /> '''Week 5. 2023-02-11 '''<br /> <br /> Lecture. Non-Stationary Time Series, Holt-Winter's exponential smoothing [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l5_done.pdf pdf]<br /> <br /> Class. ACF, PACF for ARMA(1,1), AR(2) [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w05a-arma11-pacf-interpretation online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w05b-arma11-pacf-interpretation.pdf online-B]<br /> <br /> '''Week 6. 2023-02-18 '''<br /> <br /> Lecture. ETS-model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_lecture-2023-02-18_ets-problems.pdf pdf]<br /> <br /> Class. ETS(AAA) [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w06a-ets-aaa.pdf, online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w06b-ets-aaa.pdf online-B]<br /> <br /> '''Week 7. 2023-02-25 '''<br /> <br /> Lecture. GARCH [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l7.pdf pdf]<br /> <br /> '''Week 8. 2023-03-04'''<br /> <br /> Lecture. UOL - point, interval estimators [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l8.pdf pdf], []<br /> <br /> '''Week 9. 2023-03-11 '''<br /> <br /> Lecture. UOL - Fisher info [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l9.pdf pdf]<br /> <br /> Class. Fisher info<br /> <br /> '''Week 10. 2023-03-18 '''<br /> <br /> Lecture. UOL<br /> <br /> '''Week 11. 2023-03-25 '''<br /> <br /> Midterm<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [https://github.com/bdemeshev/tssp_2022-23/tree/main/online_classes handwritten class notes]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=76811 Tssp-2022-23 2023-03-03T12:10:00Z <p>Popova.svetlana: /* Semester II */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/749646288 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> Нажми &quot;развернуть&quot;, чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣<br /> &lt;div class=&quot;mw-collapsible mw-collapsed&quot; style=&quot;width:1000px; overflow: hidden;&quot;&gt;<br /> <br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> &lt;/div&gt; <br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01a-white-noise-stationarity.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01b-white-noise-stationarity.pdf online-B]<br /> <br /> '''Week 2. 2023-01-21'''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-B]<br /> <br /> '''Week 3. 2023-01-28 '''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: recurrence equations and AR(1), Yule-Wolker equations for PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-B]<br /> <br /> '''Week 4. 2023-02-04 '''<br /> <br /> Lecture. Forecasting with ARMA, ADF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Forecasting with AR, MA(infty) solutions [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w04a-ma-infinity-solution.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w04b-ma-infinity-solution.pdf online-B]<br /> <br /> '''Week 5. 2023-02-11 '''<br /> <br /> Lecture. Non-Stationary Time Series, Holt-Winter's exponential smoothing [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l5_done.pdf pdf]<br /> <br /> Class. ACF, PACF for ARMA(1,1), AR(2) [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w05a-arma11-pacf-interpretation online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w05b-arma11-pacf-interpretation.pdf online-B]<br /> <br /> '''Week 6. 2023-02-17 '''<br /> <br /> Lecture. ETS-model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_lecture-2023-02-18_ets-problems.pdf pdf]<br /> <br /> Class. ETS(AAA) [https://github.com/bdemeshev/tssp_2022-23/blob/main/online_classes/w06a-ets-aaa.pdf, online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w06b-ets-aaa.pdf online-B]<br /> <br /> '''Week 7. 2023-02-24 '''<br /> <br /> Lecture. GARCH [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l7.pdf pdf]<br /> <br /> '''Week 8. plan '''<br /> <br /> GARCH<br /> <br /> '''Week 9. plan '''<br /> <br /> Volatility<br /> <br /> '''Week 10. plan '''<br /> <br /> Point, interval estimation<br /> <br /> '''Week 11. plan '''<br /> <br /> Total recall, midterm<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [https://github.com/bdemeshev/tssp_2022-23/tree/main/online_classes handwritten class notes]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=76526 Tssp-2022-23 2023-02-16T14:25:35Z <p>Popova.svetlana: /* Semester II */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> Нажми &quot;развернуть&quot;, чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣<br /> &lt;div class=&quot;mw-collapsible mw-collapsed&quot; style=&quot;width:1000px; overflow: hidden;&quot;&gt;<br /> <br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> &lt;/div&gt; <br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01a-white-noise-stationarity.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01b-white-noise-stationarity.pdf online-B]<br /> <br /> '''Week 2. 2023-01-21'''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-B]<br /> <br /> '''Week 3. 2023-01-28 '''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: recurrence equations and AR(1), Yule-Wolker equations for PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-B]<br /> <br /> '''Week 4. 2023-02-04 '''<br /> <br /> Lecture. Forecasting with ARMA, ADF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Forecasting with AR, MA(infty) solutions [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w04a-ma-infinity-solution.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w04b-ma-infinity-solution.pdf online-B]<br /> <br /> '''Week 5. 2023-02-11 '''<br /> <br /> Lecture. Non-Stationary Time Series, Holt-Winter's exponential smoothing [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l5_done.pdf pdf]<br /> <br /> Class. ACF, PACF for ARMA(1,1), AR(2) [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w05a-arma11-pacf-interpretation online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w05b-arma11-pacf-interpretation.pdf online-B]<br /> <br /> '''Week 6. plan '''<br /> <br /> ETS-model <br /> <br /> Class. ETS(AAA) [https://github.com/bdemeshev/tssp_2022-23/blob/main/online_classes/w06a-ets-aaa.pdf, online-A]<br /> <br /> '''Week 7. plan '''<br /> <br /> Detrending, STL<br /> <br /> '''Week 8. plan '''<br /> <br /> GARCH<br /> <br /> '''Week 9. plan '''<br /> <br /> Volatility<br /> <br /> '''Week 10. plan '''<br /> <br /> Point, interval estimation<br /> <br /> '''Week 11. plan '''<br /> <br /> Total recall, midterm<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [https://github.com/bdemeshev/tssp_2022-23/tree/main/online_classes handwritten class notes]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=76390 Tssp-2022-23 2023-02-10T20:56:50Z <p>Popova.svetlana: /* Semester II */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> Нажми &quot;развернуть&quot;, чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣<br /> &lt;div class=&quot;mw-collapsible mw-collapsed&quot; style=&quot;width:1000px; overflow: hidden;&quot;&gt;<br /> <br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> &lt;/div&gt; <br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01a-white-noise-stationarity.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01b-white-noise-stationarity.pdf online-B]<br /> <br /> '''Week 2. 2023-01-21'''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-B]<br /> <br /> '''Week 3. 2023-01-28 '''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: recurrence equations and AR(1), Yule-Wolker equations for PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-B]<br /> <br /> '''Week 4. 2023-02-04 '''<br /> <br /> Lecture. Forecasting with ARMA, ADF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Forecasting with AR, MA(infty) solutions<br /> <br /> '''Week 5. plan '''<br /> <br /> ADF, KPSS, Hyndman-Khandakar procedure, AIC<br /> <br /> '''Week 6. plan '''<br /> <br /> ETS-model <br /> <br /> '''Week 7. plan '''<br /> <br /> Detrending, STL<br /> <br /> '''Week 8. plan '''<br /> <br /> GARCH<br /> <br /> '''Week 9. plan '''<br /> <br /> Volatility<br /> <br /> '''Week 10. plan '''<br /> <br /> Point, interval estimation<br /> <br /> '''Week 11. plan '''<br /> <br /> Total recall, midterm<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [https://github.com/bdemeshev/tssp_2022-23/tree/main/online_classes handwritten class notes]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=76389 Tssp-2022-23 2023-02-10T20:56:31Z <p>Popova.svetlana: /* Semester II */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> Нажми &quot;развернуть&quot;, чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣<br /> &lt;div class=&quot;mw-collapsible mw-collapsed&quot; style=&quot;width:1000px; overflow: hidden;&quot;&gt;<br /> <br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> &lt;/div&gt; <br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01a-white-noise-stationarity.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w01b-white-noise-stationarity.pdf online-B]<br /> <br /> '''Week 2. 2023-01-21'''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-A], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w02a-acf-pacf.pdf online-B]<br /> <br /> <br /> '''Week 3. 2023-01-28 '''<br /> <br /> Lecture. ARMA process, more ACF/PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l2%263_DSBA3_2022.pdf pdf]<br /> <br /> Class: recurrence equations and AR(1), Yule-Wolker equations for PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/w03a-ar1-more-pacf.pdf online-B]<br /> <br /> '''Week 4. 2023-02-04 '''<br /> <br /> Lecture. Forecasting with ARMA, ADF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Forecasting with AR, MA(infty) solutions<br /> <br /> '''Week 5. plan '''<br /> <br /> ADF, KPSS, Hyndman-Khandakar procedure, AIC<br /> <br /> '''Week 6. plan '''<br /> <br /> ETS-model <br /> <br /> '''Week 7. plan '''<br /> <br /> Detrending, STL<br /> <br /> '''Week 8. plan '''<br /> <br /> GARCH<br /> <br /> '''Week 9. plan '''<br /> <br /> Volatility<br /> <br /> '''Week 10. plan '''<br /> <br /> Point, interval estimation<br /> <br /> '''Week 11. plan '''<br /> <br /> Total recall, midterm<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [https://github.com/bdemeshev/tssp_2022-23/tree/main/online_classes handwritten class notes]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=75756 Tssp-2022-23 2023-01-19T12:32:39Z <p>Popova.svetlana: </p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity<br /> <br /> '''Week 2. 2023-01-21'''<br /> <br /> Lecture.<br /> <br /> Class: ACF, PACF<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=75755 Tssp-2022-23 2023-01-19T12:30:41Z <p>Popova.svetlana: /* Semester II */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova, [https://t.me/LebesgueH Yan Maximov]<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity<br /> <br /> '''Week 2. 2023-01-21'''<br /> <br /> Lecture.<br /> <br /> Class: ACF, PACF<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=75754 Tssp-2022-23 2023-01-19T12:30:20Z <p>Popova.svetlana: </p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova, [https://t.me/LebesgueH Yan Maximov]<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity<br /> <br /> '''Week 1. 2023-01-21'''<br /> <br /> Lecture.<br /> <br /> Class: ACF, PACF<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=75753 Tssp-2022-23 2023-01-19T12:29:56Z <p>Popova.svetlana: /* Log Book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova, [https://t.me/LebesgueH Yan Maximov]<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> <br /> ==== Semester II ====<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity<br /> <br /> '''Week 1. 2023-01-21'''<br /> <br /> Lecture.<br /> <br /> Class: ACF, PACF<br /> <br /> == Semester II ==<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity<br /> <br /> '''Week 1. 2023-01-21'''<br /> <br /> Lecture.<br /> <br /> Class: ACF, PACF<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=75752 Tssp-2022-23 2023-01-19T12:29:17Z <p>Popova.svetlana: /* Semester I */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova, [https://t.me/LebesgueH Yan Maximov]<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> ==Log Book ==<br /> <br /> ==== Semester I ====<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> == Semester II ==<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity<br /> <br /> '''Week 1. 2023-01-21'''<br /> <br /> Lecture.<br /> <br /> Class: ACF, PACF<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=75751 Tssp-2022-23 2023-01-19T12:28:04Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova, [https://t.me/LebesgueH Yan Maximov]<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Semester I ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> <br /> == Semester II ==<br /> <br /> '''Week 1. 2023-01-14'''<br /> <br /> Lecture. Intro to Time Series, stationarity, ACF, PACF [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m3_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: White noise, stationarity<br /> <br /> '''Week 1. 2023-01-21'''<br /> <br /> Lecture.<br /> <br /> Class: ACF, PACF<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=75134 Tssp-2022-23 2022-12-27T16:38:11Z <p>Popova.svetlana: /* Teachers and assistants */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=74635 Tssp-2022-23 2022-12-04T13:59:59Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], Sveta Popova, Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class: BS model, SDE<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=74534 Tssp-2022-23 2022-11-30T11:02:06Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], Sveta Popova, Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral (Wu dWu), L2 convergence<br /> <br /> '''Week 11. 2022-11-19'''<br /> <br /> Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class: Ito's lemma<br /> <br /> '''Week 12. 2022-11-26'''<br /> <br /> Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=74117 Tssp-2022-23 2022-11-15T21:24:42Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> Actual grades: [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx xlsx], [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pubhtml html]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], Sveta Popova, Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class: Stochastic integral, L2 convergence<br /> <br /> '''Week 10. 2022-11-12'''<br /> <br /> Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=73906 Tssp-2022-23 2022-11-08T18:19:50Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx Actual grades]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], Sveta Popova, Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> Class: Solve midterm tasks<br /> <br /> '''Week 9. 2022-11-05'''<br /> <br /> Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=73541 Tssp-2022-23 2022-10-24T17:46:43Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx Actual grades]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], Sveta Popova, Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-29-class_4b-sigma-algebra.pdf 4b]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-03-class_5a-conditional-e-var.pdf 5a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-06-class_5b-conditional-e-var.pdf 5b]<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-10-class_6a-martingales.pdf 6a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-13-class_6b-martingales.pdf 6b]<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7a-poisson-process.pdf 7a], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-20-class_7b-poisson-process.pdf 7b]<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX video], [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-10-22-class_8-wiener-process.pdf pdf]<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=73540 Tssp-2022-23 2022-10-24T17:42:45Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx Actual grades]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], Sveta Popova, Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis. <br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim <br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra, [https://github.com/bdemeshev/tssp_2022-23/raw/main/online_classes/tssp-2022-09-26-class_4a-sigma-algebra.pdf 4a]<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX]<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=73537 Tssp-2022-23 2022-10-24T14:07:30Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx Actual grades]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], Sveta Popova, Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis.<br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim<br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> Class: Martingales, filtration<br /> <br /> '''Week 7. 2022-10-15'''<br /> <br /> Lecture. Brownian motion (Wiener process), filtration in continuous time [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l7_DSBA3_2022.pdf pdf]<br /> <br /> Class: Poisson process<br /> <br /> '''Week 8. 2022-10-22'''<br /> <br /> Lecture. Wiener process (additional exercises) [https://zoom.us/rec/share/19THFlfbJsToxi_xiEt3sdXcCtkgbcfBneKULjwjLDUGfBnJDgHSR4Z3EHDKCWA_.t93R85fWqce0aImX]<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=73164 Tssp-2022-23 2022-10-10T09:59:02Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> [https://docs.google.com/spreadsheets/d/e/2PACX-1vQqRvmRVq-b2olpbl_028gjSsoVdR2cANoTyDXhaYjTCQELwjECBgj2oljHsXC8XlkGrXx2up-ebrAh/pub?output=xlsx Actual grades]<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], Sveta Popova, Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis.<br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim<br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra<br /> <br /> '''Week 6. 2022-10-08'''<br /> <br /> Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=72927 Tssp-2022-23 2022-10-03T11:10:05Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], Sveta Popova, Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis.<br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim<br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/Lect_04_TSSP%20(2).pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra<br /> <br /> '''Week 5. 2022-10-01'''<br /> <br /> Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]<br /> <br /> Class. Conditional expectation and variance, sigma algebra<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana https://wiki.cs.hse.ru/index.php?title=Tssp-2022-23&diff=72797 Tssp-2022-23 2022-09-27T12:19:19Z <p>Popova.svetlana: /* Log-book */</p> <hr /> <div>== General course info ==<br /> <br /> * Boring [https://www.hse.ru/edu/courses/395889616 official] web page<br /> <br /> ==== Grading ====<br /> <br /> Fall grade = 0.3 HAs + 0.7 October Exam<br /> <br /> Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam<br /> <br /> ==== Teachers and assistants ====<br /> <br /> Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] <br /> <br /> Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], Sveta Popova, Maria Kirillova<br /> <br /> ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====<br /> <br /> == Log-book ==<br /> <br /> '''Week 1. 2022-09-03'''<br /> <br /> Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]<br /> <br /> Class. Transition matrix, first step analysis.<br /> <br /> More:<br /> <br /> [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> '''Week 2. 2022-09-10'''<br /> <br /> Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf]<br /> <br /> Class. Stationary distribution, modes of convergence<br /> <br /> '''Week 3. 2022-09-17'''<br /> <br /> Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]<br /> <br /> Class. plim, almost sure lim<br /> <br /> '''Week 4. 2022-09-24'''<br /> <br /> Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/Lect_04_TSSP%20(2).pdf pdf]<br /> <br /> Class. Conditional expectation, sigma algebra<br /> <br /> == Sources ==<br /> * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]<br /> * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22]<br /> * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]<br /> * [https://t.me/TSSP22 TG chat 2022-23]<br /> * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]<br /> <br /> * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]<br /> <br /> === MC + MCMC ===<br /> <br /> * James Norris, Markov chains (1998, no kernels)<br /> <br /> * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains<br /> <br /> * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]<br /> <br /> * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]<br /> <br /> * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]<br /> <br /> * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]<br /> <br /> * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)<br /> <br /> === Stochastic Calculus ===<br /> <br /> * Zastawniak, Basic Stochastic Processes<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]<br /> <br /> * [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]<br /> <br /> * [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]<br /> <br /> === Time Series ===<br /> <br /> * [https://otexts.com/fpp3/ Forecasting principles and practice (R)]<br /> <br /> * [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]<br /> <br /> * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]<br /> <br /> * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]<br /> <br /> * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]<br /> <br /> ==== UCM ====<br /> <br /> * Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]<br /> <br /> * João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]<br /> <br /> * [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]<br /> <br /> * [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]<br /> <br /> * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]</div> Popova.svetlana