|Learning resources||OLAT Learning Management System (also via guest access)|
|Primary reference||Cryer & Chan KS (2008). Time Series Analysis - With Applications in R, 2nd ed. Springer-Verlag.
R package, Springer homepage
|Secondary reference||Kleiber & Zeileis (2008). Applied Econometrics with R. Springer-Verlag.
R package, Chapter 1, Chapter 2, Springer homepage, Google books
- Smoothing and decomposition methods
- Stochastic processes
- ARIMA models
- Stationarity, unit roots, and cointegration
- Time series regression and structural change
- GARCH models
- Multivariate time series models
- Linear regression
- Ordinary/weighted/generalized least squares estimation
- Gauss-Markov theorem
- Inference (t and F tests) for linear hypotheses
- Robust standard errors
- Regression diagnostics
- Factors and interactions
- Model selection
The R system for statistical computing will be used throughout the lecture. All methods and their application will be illustrated using R. Exercises should be solved using R.
Installation under Windows: Base R.
- Integrated development environment for R: RStudio
- Introduction to R
Topics include: An introductory R session, Getting started, getting help, basic data management, a tour of exploratory data analysis, regression, and some programming issues.