Tssp-2022-23
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
Week 2. 2023-01-21
Lecture.
Class: ACF, PACF
Week 3. plan
ARMA process, more ACF/PACF
Week 4. plan
Forecasting with ARMA
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