Time Series and Stochastic Processes ada 21 22 — различия между версиями
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Bdemeshev (обсуждение | вклад) (→Semester II) |
Bdemeshev (обсуждение | вклад) (→Semester II) |
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= Semester II = | = Semester II = | ||
− | Do not forget about [https://github.com/bdemeshev/tssp_2021-22/raw/main/ha/tssp_ha.pdf the home assignments | + | Do not forget about [https://github.com/bdemeshev/tssp_2021-22/raw/main/ha/tssp_ha.pdf the home assignments!] |
==== Week 1 ==== | ==== Week 1 ==== | ||
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2.2. AR(2), expected value, covariances: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_a.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_b.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_c.pdf pdf-c] | 2.2. AR(2), expected value, covariances: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_a.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_b.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_c.pdf pdf-c] | ||
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+ | [https://github.com/bdemeshev/tssp_2021-22/raw/main/arma_no_nonsense/arma_no_nonsense.pdf Arma notes without nonsense] | ||
==== Week 3 ==== | ==== Week 3 ==== |
Текущая версия на 18:08, 12 февраля 2022
General course info
- Boring official web page
- teams group: all class videos are there :)
Teachers and assistants
Lecturer: Peter Lukianchenko
Class teacher: Boris Demeshev
Semester I
Semester II
Do not forget about the home assignments!
Week 1
Lecture 1. White noise, stationarity, ACF, PACF
1.1.
1.2. Predictive interval for random walk, difference between mean, mode and median: pdf-b
Week 2
2.1. ETS model, forecasting, decomposition: pdf-a, pdf-b, pdf-c
2.2. AR(2), expected value, covariances: pdf-a, pdf-b, pdf-c
Week 3
3.1. Non stationarity of ETS(AAA), solutions of recurrence equation: pdf-b
3.2. Equations is not a process. Two problems from Econometrics Olympiad: pdf-a, pdf-b, pdf-c.
Week 4
4.1. Solutions of recurrence equation: pdf-a, pdf-b, pdf-c.
4.2. Roots of lag and characteristic equation: pdf-a, pdf-b, pdf-c.
Week 5
Estimation of ETS and ARMA: colab notebook
Week 6
Sources
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