Time Series and Stochastic Processes ada 21 22
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Версия от 19:45, 4 февраля 2022; Bdemeshev (обсуждение | вклад)
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
Lecture 2: Examples of ARMA models.
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.
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