Time Series Analysis
Extra Office Hours (room tba, please come to Prof. Fitzenberger's chair in KG II):
- Dr. S. Kestel: Friday, 29 July 2011, 10:00-12:00 & Mo, 1 August 2011, 10:00-12:00
- Mr. D. Ruf: Fr, 15 July 2011, 12:00-16:00 & Mo, 18 July 2011, 10:00-12:00
Credits: 4 CP
Course Outline
The study of the sequence of data points measured at successive times enables us to often either to understand the underlying theory of the data points (where did they come from what generated them), or to make forecasts (predictions). Time series prediction is the use of a model to predict future events based on known past events: to predict future data points before they are measured.
The objective of the course is to provide students to learn time series modelling in theory and practice. The course will start with reviewing the fundamental concepts in regression analysis. Autocorrelation function, Linear Stationary models: General linear process, Autoregressive, Moving averages, ARMA processes, Non-stationary models: Autoregressive Integrated Moving Average and Integrated Moving Average processes, Forecasting: Minimum Mean Square Error Forecast, updating forecasts, Stochastic Model building: Model identification, Model estimation (maximum likelihood estimation, nonlinear estimation, Bayes’ estimation), Model diagnostic checking, Seasonal models, Vector Autoregressive Models, and cointegration will be covered.
Literature
- Enders, W., Applied Econometric Time Series, Second Edition, Wiley
- Kirchgässner, Wolters, Introduction to Modern Time Series, First Edition, Springer Verlag
- Shumway, R.H., Stoffer D.S., Time Series Analysis and its Applications, 2nd Ed. Springer
- Lecture Notes
- Materials
- Classical Time Series Example: Excel table
- Old- Final Examination
- Formula
- Assignments
- A1
Tutorials
- Tutorial 1
- Tutorial 2
- Tutorial 3
- Tutorial 4