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Time Series Econometrics

Current Issues

The news for Time Series Econometrics can be found on the German page of the course.


Course Contents

Course Description

The course Time Series Econometrics mainly deals with the analysis of time series data. The latter in general requires a weakening of some assumptions for the linear regression model established in the course Introductory Econometrics. Henceforth, the exact estimation properties for a given sample are replaced by asymptotic or large sample properties. This approach is also necessary, when statistical tests with unknown error distribution are performed. Furthermore it is shown how the asymptotic properties depend on the dynamic stability properties of the stochastic mechanism which could have generated the observed data, and how this so called stationarity properties for autoregressive time series models can be checked. Closely related to this is the problem whether an observed time trend is of deterministic nature or an occurrence of a random walk. For answering this question unit root tests are required. In the second part of the course, multivariate time series models allowing for the analysis of multiple time series and their interdependencies are introduced. If several time series are influenced by time trends, there exist long-term equilibria that are modeled using a cointegration approach. These models particularly play an important role in empirical macroeconomics and are an indispensable tool for predictions. 


Course Outline

1. Introduction and overview

2. Regression models for time series I: Regression models with strictly exogenous regressors

3. Trends and seasonality

4. Regression models for time series II: Autoregressive models - lagged endogenous variables as regressors

5. Asymptotic properties of the OLS estimator: Estimation properties of the OLS estimator if the classical OLS assumptions are violated

6. Nonstationary time series processes: Random walk processes and more

7. Regression models for time series III: Dynamic regression models - regressors uncorrelated with the contemporary error

8. Regression models for time series IV: Regression models with autocorrelated and heteroskedastic errors

9. Tests for checking the random walk hypothesis: Unit root tests

10. Cointegration, vector error correction models and vector autoregressive models

11. Forecasting 


Literature

Wooldridge, J.M. (2009). Introductory Econometrics. A Modern Approach, 4. edition, Thomson South-Western (Chapters 5, 7, 9, 10-12, 18). 


Audience / Qualification

This course is for bachelor students in the 2nd study phase having already attended the course Introductory Econometrics. 


Grading System

The grading system can be found on the German page of the course.


Downloads

The course material for Time Series Econometrics can be found on the German page of the course.


Appointments and Rooms

The schedule can be found on the German page of the course.



  1. Faculty of Business, Economics and Management Information Systems
  2. Department of Economics

Chair of Econometrics