Time Series Analysis
The course is taught to PhD & Masters students and considers the use of modern time series methods. Topics covered include an introduction to the dynamic properties of time series, stochastic difference equations, stationary univariate models, forecast evaluation, state-space models, non-stationary models and unit roots, vector autoregression models, structural vector autoregression models, Bayesian vector autoregression models, cointegration and error-correction, dynamic factor models and factor augmented vector autoregression models, heteroskedastic and stochastic volatility models, as well as nonlinear regime-switching models. Throughout the course we will emphasize areas of ongoing research.
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Course outline [link]
1) Introduction [notes] [slides] [tutorial] [R files]
2) Structural breaks [notes] [slides] [tutorial] [R files]
3) Univariate autoregressive moving average models [notes] [slides] [tutorial] [R files]
4) Forecasting and out-of-sample evaluations [notes] [slides] [tutorial] [R files]
5) Univariate state-space models [notes] [slides] [tutorial] [R files]
6) Decompositions and spectral analysis [notes] [slides] [tutorial] [R files]
7) Nonstationarity and unit root tests [notes] [slides] [tutorial] [R files]
8) Univariate volatility models [notes] [slides] [tutorial] [R files]
9) Nonlinear regime-switching models [notes] [slides] [tutorial] [R files]
10) Autoregressive distributed lag models [notes] [slides] [tutorial] [R files]
11) Vector autoregression models [notes] [slides] [tutorial] [R files]
12) Structural vector autoregression models [notes] [slides] [tutorial] [R files]
13) Bayesian vector autoregression models [notes] [slides] [tutorial] [R files]
14) Cointegration and error correction models [notes] [slides] [tutorial] [R files]
15) Dynamic factor models [notes] [slides] [tutorial] [R files]
16) Multivariate volatility models [notes] [slides] [tutorial] [R files]
17) Nonlinear regime-switching models [notes] [slides] [tutorial] [R files]
18) Introduction to statistical learning methods
19) Clustering models
20) High-dimensional models
21) Regression trees
22) Random forest and boosting
23) Deep learning models [notes] [slides] [tutorial] [R files]
Appendix A: Mathematics [notes]
Appendix B: Probability and Statistics [notes]
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