Applied univariate and multivariate time series analysis

Inhalt:

Roadmap:

1. Basics: Difference Equations, Solutions, Lag Operators

2. Stationary Time-Series Models: ARMA (p,q), ACF/PACF, Box-Jenkins

3. Identification Problems in Macroeconometrics

4. Models with Trend: Dickey-Fuller-Test, Structural Change, Panel Unit Root tests

5. Cointegration and Error-Correction Models

6. Some Non-linear Time-Series Models

Lernziel:

Students will be enabled to apply macroeconometric techniques to a variety of cases. Students

are encouraged to bring their own problems and data sets to analyze them in the course.

Students will be enabled to use the software RATS.

Vorgehen:

The course is a mixture of lectures, practical exercises and programming RATS code and own

empirical work.

Literatur:

Basic References:

@1: Enders (2010), ch. 1; Kirchgässner, Wolters (2007), ch. 1.

@2: Enders (2010), ch. 2; Kirchgässner, Wolters (2007), ch. 2.

@3: Favero (2001), ch. 3, ch. 4, ch. 6; Kirchgässner, Wolters (2007), ch. 4; Enders (2010)3, ch. 5.

@4: Enders (2010), ch. 4; Kirchgässner, Wolters (2007), ch. 5.

@5: Enders (2010), ch. 6; Kirchgässner, Wolters (2007), ch. 6.

@6: Enders (2010), ch. 7.

Books:

Walter Enders (2010): Applied Econometric Time Series, 3rd edition, Wiley.

Carlo A. Favero (2001): Applied Macroeconometrics, Oxford University Press.

Gebhard Kirchgässner, Jürgen Wolters (2007): Introduction to Modern Time Series Analysis,

Springer.

RATS: www.estima.com

Zusätzliche Hinweise zu Prüfungen:

Students will work on empirical projects (either own projects or tasks defined in the course). A

written documention of the empirical project will be graded.