Time Series and Stochastic Processes ada 20 21 — различия между версиями

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== General course info ==
 
== General course info ==
  
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* Boring [https://www.hse.ru/edu/courses/383218629 Official] web page
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* Boring [https://www.hse.ru/edu/courses/383218629 official] web page
  
 
* [https://t.me/joinchat/DtwHDEbRczyglTC1Z-W-Ug tg-channel]
 
* [https://t.me/joinchat/DtwHDEbRczyglTC1Z-W-Ug tg-channel]
  
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== Week progress ==
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* [https://github.com/bdemeshev/tssp/raw/master/ha/tssp_ha.pdf All home Assignments]
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* [https://docs.google.com/spreadsheets/d/1PQmrMM9usDrDs2oAjJJpGCUmFw6B53pDg5T4VBZmm4s/edit?usp=sharing Grades]
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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.
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= Teachers and assistants =
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{| class="wikitable" style="text-align:center"
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|-
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! Group!! БПАД191 !! БПАД192 !! БПАД193
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|-
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|| Lecturer ||colspan="3"| [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko]
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|-
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|| Teacher ||colspan="3"| [https://www.hse.ru/staff/bbd Boris Demeshev]
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|}
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= Week progress =
  
 
==== Week 01 ====
 
==== Week 01 ====
  
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* Sigma-algebras, measurability of random variable with respect to sigma-algebra
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* Sigma-algebras, measurability of random variable with respect to sigma-algebra.
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* HA-01
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* Seminar 01
  
 
==== Week 02 ====
 
==== Week 02 ====
  
 
* Markov chain. Classification of states. Calculations of return probability, mean return time, stationary distribution.
 
* Markov chain. Classification of states. Calculations of return probability, mean return time, stationary distribution.
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* Seminar 02a, [https://youtu.be/WBKkk0iqysU?list=PL1poMUvVlAqfu6D4gaA_c4fJiIfsWbjzV 02b]
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* Cambridge [http://www.statslab.cam.ac.uk/~rrw1/markov/ Markov chain course]
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* 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 ====
 
==== Week 03 ====
  
 
* Conditional expected value. Martingales.
 
* Conditional expected value. Martingales.
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==== Week 04 ====
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* [https://youtu.be/mJmbcp5h7lo?list=PL1poMUvVlAqfu6D4gaA_c4fJiIfsWbjzV Abracadabra martingale]
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==== Week 05 ====
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* Wiener process: basic properties, inversion
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==== Week 06 ====
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* Wiener process: limit in L2
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==== Week 07 ====
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[https://www.youtube.com/watch?v=yTCI-Ng76OU Ito integral WtdWt], Ito's lemma
  
 
== Sources ==
 
== Sources ==
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=== MC + MCMC ===
 
=== MC + MCMC ===
 
* James Norris, Markov chains (1998, no kernels)
 
* James Norris, Markov chains (1998, no kernels)
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* [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]
 
* [https://eml.berkeley.edu/reprints/misc/understanding.pdf Chib and Greenberg, Understanding MH algorithm]
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* [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf Charles Geyer, MCMC lecture notes (with a little bit of kernels!)]
 
* [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf Charles Geyer, MCMC lecture notes (with a little bit of kernels!)]
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== Grading System ==
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Interim assessment (2 module):<br>
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0.400 FallMock<br>
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0.400 Winter Mock<br>
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0.200 Homework<br>
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Interim assessment (4 module): <br>
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0.650 UoL Exam<br>
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0.100 Final Exam<br>
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0.100 Homework<br>
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0.100 Spring Mock<br>
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0.050 Quizzes<br>

Текущая версия на 12:59, 9 апреля 2021

General course info

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

Group БПАД191 БПАД192 БПАД193
Lecturer Peter Lukianchenko
Teacher 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, 02b
  • Cambridge Markov chain course. There you may find useful: lecture notes, past tripos and more.

Week 03

  • Conditional expected value. Martingales.

Week 04

Week 05

  • Wiener process: basic properties, inversion

Week 06

  • Wiener process: limit in L2

Week 07

Ito integral WtdWt, Ito's lemma

Sources

Stochastic Calculus

  • Zastawniak, Basic Stochastic Processes

Time Series

UCM

MC + MCMC

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

Grading System

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

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

Источник — «http://wiki.cs.hse.ru/index.php?title=Time_Series_and_Stochastic_Processes_ada_20_21&oldid=53058»