Просмотр исходного текста страницы Tssp-2022-23

== General course info ==

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

==== Grading ====

Fall grade = 0.3 HAs + 0.7 October Exam

Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam

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]

==== Teachers and assistants ====

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

Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova

==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ====

==Log Book ==

==== Semester I  ====

'''Week 1. 2022-09-03'''

Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf]

Class. Transition matrix, first step analysis. 

More:

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

'''Week 2. 2022-09-10'''

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]

Class. Stationary distribution, modes of convergence

'''Week 3. 2022-09-17'''

Lecture. Markov process, math modelling, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l3_DSBA3_2022.pdf pdf]

Class. plim, almost sure lim 

'''Week 4. 2022-09-24'''

Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf]

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]

'''Week 5. 2022-10-01'''

Lecture. First-step analysis, sigma algebra [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l5_DSBA3_2022.pdf pdf]

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]

'''Week 6. 2022-10-08'''

Lecture. Basics of stochastic processes  [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf]

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]

'''Week 7. 2022-10-15'''

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]

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]

'''Week 8. 2022-10-22'''

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]

Class: Solve midterm tasks

'''Week 9. 2022-11-05'''

Lecture. Stochastic integral, Ito formula [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l1_DSBA3_2022.pdf pdf]

Class: Stochastic integral, L2 convergence

'''Week 10. 2022-11-12'''

Lecture. Ito's lemma, BS model [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l2_DSBA3_2022.pdf pdf]

Class: Stochastic integral (Wu dWu), L2 convergence

'''Week 11. 2022-11-19'''

Lecture. BS solution [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l3_DSBA3_2022.pdf pdf]

Class: Ito's lemma

'''Week 12. 2022-11-26'''

Lecture. Binomial tree, risk-neutral probability [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m2_l4_DSBA3_2022.pdf pdf]

Class: BS model, SDE


==== Semester II ====

'''Week 1. 2023-01-14'''

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]

Class: White noise, stationarity

'''Week 2. 2023-01-21'''

Lecture.

Class: ACF, PACF

== Sources ==
* [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams]
* [https://github.com/bdemeshev/tssp_2022-23/tree/main/online_classes handwritten class notes]
* [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]
* [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22]
* [https://t.me/TSSP22 TG chat 2022-23]
* [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском]

* [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]