Просмотр исходного текста страницы Time Series and Stochastic Processes ada 20 21

== General course info ==

* Boring [https://www.hse.ru/edu/courses/383218629 official] web page

* [https://t.me/joinchat/DtwHDEbRczyglTC1Z-W-Ug tg-channel]

* [https://github.com/bdemeshev/tssp/raw/master/ha/tssp_ha.pdf All home Assignments]

* [https://docs.google.com/spreadsheets/d/1PQmrMM9usDrDs2oAjJJpGCUmFw6B53pDg5T4VBZmm4s/edit?usp=sharing Grades]

This course is conducted at Data Science and Business Analytics program and is provided to 3rd-year undergraduates who have studied a course covering basic probability and statistical inference. A half of this course introduces concepts of Markov chains, random walks, martingales as well as of to the time series. The course requires basic knowledge in probability theory and linear algebra. It introduces students to the modeling, quantification and analysis of uncertainty. The main objective of this course is to develop the skills needed to do empirical research in fields operating with time series data sets. The course aims to provide students with techniques and receipts for estimation and assessment of quality of economic models with time series data. The course will also emphasize recent developments in Time Series Analysis and will present some open questions and areas of ongoing research.

= Teachers and assistants =

{| class="wikitable" style="text-align:center"
|-
! Group!! БПАД191 !! БПАД192 !! БПАД193
|-
|| Lecturer ||colspan="3"| [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] 
|-
|| Teacher ||colspan="3"| [https://www.hse.ru/staff/bbd Boris Demeshev]
|}

= Week progress =

==== Week 01 ====

* Sigma-algebras, measurability of random variable with respect to sigma-algebra.
* Seminar 01

==== Week 02 ====

* Markov chain. Classification of states. Calculations of return probability, mean return time, stationary distribution.
* Seminar 02a, [https://youtu.be/WBKkk0iqysU?list=PL1poMUvVlAqfu6D4gaA_c4fJiIfsWbjzV 02b]
* Cambridge [http://www.statslab.cam.ac.uk/~rrw1/markov/ Markov chain course]. There you may find useful: [http://www.statslab.cam.ac.uk/~rrw1/markov/M.pdf lecture notes], [http://www.statslab.cam.ac.uk/~rrw1/markov/MarkovChainTriposQuestions.pdf past tripos] and more.

==== Week 03 ====

* Conditional expected value. Martingales.

==== Week 04 ====

* [https://youtu.be/mJmbcp5h7lo?list=PL1poMUvVlAqfu6D4gaA_c4fJiIfsWbjzV Abracadabra martingale]

==== Week 05 ====

* Wiener process: basic properties, inversion

==== Week 06 ====

* Wiener process: limit in L2

==== Week 07 ====

[https://www.youtube.com/watch?v=yTCI-Ng76OU Ito integral WtdWt], Ito's lemma

== Sources ==

=== 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]

* [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html van der Vaart]

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

==== UCM ====

* [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html aa]

* [https://core.ac.uk/download/pdf/6242335.pdf bb]

* [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]

=== MC + MCMC ===
* James Norris, Markov chains (1998, no kernels)

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

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

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

* [http://www.statslab.cam.ac.uk/~rrw1/markov/index.html  (no kernels)]

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

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

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

== Grading System ==

Interim assessment (2 module):<br>
0.400	FallMock<br>
0.400	Winter Mock<br>
0.200	Homework<br>

Interim assessment (4 module): <br>
0.650	UoL Exam<br>
0.100	Final Exam<br>
0.100	Homework<br>
0.100	Spring Mock<br>
0.050	Quizzes<br>