Просмотр кода страницы Time Series and Stochastic Processes ada 21 22

== General course info ==

* Boring [https://www.hse.ru/edu/courses/395889616 official] web page

* [https://t.me/joinchat/YUGJM3oSEnkzYjli tg-channel]

* [https://teams.microsoft.com/l/team/19%3AiJploPD703nORitAfMPy5o3cDrE92YGdsbcKy_I5Ac41%40thread.tacv2/conversations?groupId=57448dd0-a6db-4e2b-8ec8-96df322bb152&tenantId=21f26c24-0793-4b07-a73d-563cd2ec235f teams group]: all class videos are there :)

* [https://github.com/bdemeshev/tssp_2021-22/raw/main/ha/tssp_ha.pdf All home Assignments]

* Notes for [https://github.com/bdemeshev/tssp_2021-22/tree/main/lectures lectures] and [https://github.com/bdemeshev/tssp_2021-22/tree/main/notes classes].

= Teachers and assistants =

Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] 

Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev]



= Semester I =

<div class="mw-collapsible mw-collapsed" style="width:1000px; overflow: hidden;">

==== Week 01 ====

Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_01_TSSP.pdf]

Class: First step analysis, expected time to get HTH.

==== Week 02 ====

Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_02_TSSP.pdf]

Class: Markov chain states classification


==== Week 03 ====

Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_03_TSSP.pdf]

Class: Poisson process. 

==== Week 04 ====

Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_04_TSSP.pdf]

Class: Conditional expected value. Conditional variance.

==== Week 05 ====

Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_05_TSSP.pdf]

Class: Sigma-algebras, measurability. Conditional expected value with respect to sigma-algebra.

==== Week 06 ====

Lecture:

Class: Probability limit, Moment generating function


==== Midterm ====

The long-awaited midterm will be on 28 October, 10:00 - 12:00.

Duration: 120 minutes. No proctoring.

Topics:
* First step analysis
* Classification of states and classes of MC.
* Conditional expected value (two views).
* Poisson process.
* Sigma algebras.
* Probability limit
* Moment generating function


==== Week  ====

Date: 2021-10-28

Lecture:

Class: Martingales in discrete time


==== Week  ====

Date: 2021-11-09

Lecture:

Class: Wiener process definition, basic properties, inversion

==== Week  ====

Date: 2021-11-16

Lecture:

Class: Stochastic integral, intuition, limit in L2

==== Week  ====

Date: 2021-11-23

Lecture:

Class: Stochastic integral properties, Ito's lemma

==== Week  ====

Date: 2021-11-30

Lecture:

Class: BS model, Girsanov theorem, pricing

==== Week  ====

Date: 2021-12-07

Lecture:

Class: more pricing examples in BS model

==== Week  ====

Date: 2021-12-14

Lecture:

Class: Recap on martingales, Ito, etc

</div>

= Semester II =

Do not forget about [https://github.com/bdemeshev/tssp_2021-22/raw/main/ha/tssp_ha.pdf the home assignments!]

==== Week 1 ====

[https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l1_done.pdf Lecture 1]. White noise, stationarity, ACF, PACF

1.1.

1.2. Predictive interval for random walk, difference between mean, mode and median: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-14-sem2_class_01_2_b.pdf pdf-b]

==== Week 2 ====

[https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l2_done.pdf Lecture 2]. 

2.1. ETS model, forecasting, decomposition: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-18-sem2_class_02_1_a.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-18-sem2_class_02_1_b.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-18-sem2_class_02_1_c.pdf pdf-c]

2.2. AR(2), expected value, covariances: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_a.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_b.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_c.pdf pdf-c]

[https://github.com/bdemeshev/tssp_2021-22/raw/main/arma_no_nonsense/arma_no_nonsense.pdf Arma notes without nonsense]

==== Week 3 ====

[https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l3_done.pdf Lecture 3]. 

3.1. Non stationarity of ETS(AAA), solutions of recurrence equation: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-26-sem2_class_03_1_b.pdf, pdf-b]

3.2. Equations is not a process. 
Two problems from [https://new.universiade-ecm.com/ Econometrics Olympiad]: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-27-sem2_class_03_2_a_rus.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-27-sem2_class_03_2_b_eng.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-28-sem2_class_03_2_c_rus.pdf pdf-c].

==== Week 4 ====

[https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l4_done.pdf Lecture 4]. 

4.1. Solutions of recurrence equation: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-01-sem2_class_04_1_a_rus_arma_sols.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-01-sem2_class_04_1_b_rus_arma_sols.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-01-sem2_class_04_1_c_eng_arma_sols.pdf pdf-c].

4.2. Roots of lag and characteristic equation: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-03-sem2_class_04_2_a_eng_roots.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-03-sem2_class_04_2_b_rus_roots.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-03-sem2_class_04_2_c_rus_roots.pdf pdf-c].

==== Week 5 ====

[https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l5_done.pdf Lecture 5] 

Estimation of ETS and ARMA: [https://colab.research.google.com/drive/1LE9T0KnnUBM-1OIzWT3INJzJjKd5-GdX?usp=sharing colab notebook]

==== Week 6 ====






== Sources ==

* [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-2021]

* [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-2021]
* [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-2022]

* [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook]

=== MC + MCMC ===

* James Norris, Markov chains (1998, no kernels)

* [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains

* Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm]

* Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler]

* Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains]

* [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]

* Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!)

=== Stochastic Calculus ===

* Zastawniak, Basic Stochastic Processes

* [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf Exams of ICEF master course]

* [https://bdemeshev.github.io/sc401/ Заметки магистерского курса МИЭФ (рус)]

* [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Черновик учебника (рус)]

* [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]

=== Time Series ===

* [https://otexts.com/fpp3/ Forecasting principles and practice (R)]

* [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway, Stoffer Time Series Analysis]

* [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page]

* Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series]

* [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)]

==== UCM ====

* Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]

* João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]

* [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]

* [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton]

* [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]

== Grading System ==