Time Series and Stochastic Processes ada 21 22
Материал из Wiki - Факультет компьютерных наук
- 1 General course info
- 2 Teachers and assistants
- 3 Semester I
- 4 Semester II
General course info
- Boring official web page
- teams group: all class videos are there :)
Teachers and assistants
Lecturer: Peter Lukianchenko
Class teacher: Boris Demeshev
Do not forget about the home assignments!
Lecture 1. White noise, stationarity, ACF, PACF
1.2. Predictive interval for random walk, difference between mean, mode and median: pdf-b
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
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.
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.
Estimation of ETS and ARMA: colab notebook
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!)
- Zastawniak, Basic Stochastic Processes
- Van der Vaart, Time Series
- Harvey Jaeger, Detrending, Stylized Facts and the Business Cycle
- João Tovar Jalles, Structural Time Series Models and the Kalman Filter