Tssp-2022-23 — различия между версиями
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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] | 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] | ||
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'''Week 3. 2023-01-28 ''' | '''Week 3. 2023-01-28 ''' |
Версия 23:56, 10 февраля 2023
Содержание
General course info
- Boring 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
Teachers and assistants
Lecturer: Peter Lukianchenko
Class teacher: Boris Demeshev, Sveta Popova, Maria Kirillova
Home assignments
Log Book
Semester I
Нажми "развернуть", чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣
Semester II
Week 1. 2023-01-14
Lecture. Intro to Time Series, stationarity, ACF, PACF pdf
Class: White noise, stationarity online-A, online-B
Week 2. 2023-01-21
Lecture. ARMA process, more ACF/PACF pdf
Class: ACF, PACF online-A, online-B
Week 3. 2023-01-28
Lecture. ARMA process, more ACF/PACF pdf
Class: recurrence equations and AR(1), Yule-Wolker equations for PACF online-a, online-B
Week 4. 2023-02-04
Lecture. Forecasting with ARMA, ADF pdf
Class. Forecasting with AR, MA(infty) solutions
Week 5. plan
ADF, KPSS, Hyndman-Khandakar procedure, AIC
Week 6. plan
ETS-model
Week 7. plan
Detrending, STL
Week 8. plan
GARCH
Week 9. plan
Volatility
Week 10. plan
Point, interval estimation
Week 11. plan
Total recall, midterm
Sources
- all past exams
- handwritten class notes
- Wiki 2020-21, Wiki 2021-22
- Git repo 2020-21, Git repo 2021-22
- TG chat 2022-23
- видео семинаров 2022-23 на русском
MC + MCMC
- James Norris, Markov chains (1998, no kernels)
- Cambridge course on Markov chains
- Chib and Greenberg, Understanding MH algorithm
- Casella, Explaining Gibbs Sampler
- Roberts and Rosenthal, General State Space Markov Chains
- Charles Geyer, MCMC lecture notes (with a little bit of kernels!)
Stochastic Calculus
- Zastawniak, Basic Stochastic Processes
Time Series
- Van der Vaart, Time Series
UCM
- Harvey Jaeger, Detrending, Stylized Facts and the Business Cycle
- João Tovar Jalles, Structural Time Series Models and the Kalman Filter