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.
This particular page is work in progress so many of the links will not work.
The previous edition of the course has fully functional links and may be viewed here.
Course outline [link]
9) Autoregressive distributed lag models [notes] [slides] [tutorial] [R files]
14) Dynamic factor models [notes] [slides] [tutorial] [R files]
15) Multivariate volatility models [notes] [slides] [tutorial] [R files]
17) Regression trees and clustering models [notes]
18) Deep learning models [notes]
Appendix A: Mathematics [notes]
Appendix B: Probability and Statistics [notes]
Use Chrome to print the HTML documents. You may wish to scale the document to 85% when doing so.