Time Series Analysis

Instructor Achim Zeileis
Timeline Course Catalog
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

Contents

  • Introduction
  • 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

Requirements

  • 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

Software

  • R
    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.