| Course language | Frequency | Hours per week | ECTS | Exam |
|---|---|---|---|---|
| English | Summer semester | 2L+2E | 6 | Presentations Intermediate exam (30 minutes) Written exam (90 minutes) |
The Advanced Econometrics course builds on the Master's course Methods of Econometrics and teaches econometric methods that significantly expand and go beyond the applicability of the multiple regression model. Both the underlying models and the properties of the estimation methods presented are analyzed. Graduates of the course should be able to acquire adequate econometric methods for more demanding empirical analyses.
The course initially covers nonlinear regression models as used for forecasting, modeling regime transitions, and estimating microeconomic behavioral and technology equations. Panel data models are also covered. A widespread problem in empirical economic research is the endogeneity of explanatory variables, for example in simultaneous systems, in the case of omitted regressors, or in the case of measurement errors with regard to explanatory variables. Despite the resulting correlation between the regressor and the error term, which leads to the inconsistency of standard least squares methods, causal effects or structurally interpretable relationships can be identified under suitable assumptions using instrumental variable estimators.
Furthermore, more general econometric estimation principles are discussed, which can be applied to a variety of questions, even beyond regression models. Generalised moment estimation (GMM ) does not require a complete specification of the data-generating process and has become particularly popular for the estimation of individual behavioural equations of (dynamic) economics models. In contrast, the maximum likelihood (ML) principle utilises all information resulting from the specification of the entire distribution of the model variable(s). If such assumptions are appropriate, maximum estimation precision can be expected, which is why ML estimation has the status of a standard tool in the empirical sciences.
An essential part of the course is the application of the methods to economics problems. Examples covered include non-linear microeconomic cost functions in the energy sector, forecasting stock returns with non-linear smooth transition regression models, the demand for cigarettes and related tax effects in a panel data set, the relationship between (endogenous) institutions and economic development using instrumental variables, estimating the New Keynesian Phillips curve with GMM and determining the demand for doctor's visits in count data regression models with ML. The free statistical software R and its available packages are used.
CONCLUSION
- Repetition and motivation
- Non-linear regression
- Panel data models (fixed-effects estimators, random-effects estimators)
- Instrument variable estimation
- Generalised Method of Moments (GMM)
- Maximum likelihood estimation
LITERATURE
Davidson, R. and MacKinnon, J.G., Econometric Theory and Methods. Oxford University Press, 2004.
SUPPLEMENTARY LITERATURE (in alphabetical order)
Angrist, J. and Pischke, J., Mostly harmless econometrics. An Empiricist's Companion. Princeton University Press, 2009.
Davidson, J., Econometric Theory. Blackwell Publishers, 2000.
Davidson, R. & MacKinnon, J., Estimation and Inference in Econometrics. Oxford University Press, 1993.
Hansen, B.E., Econometrics. Manuscript, 2012 (external link, opens in a new window).
Hayashi, F., Econometrics. Princeton University Press, 2000.
Peracchi, F., Econometrics . John Wiley and Sons, 2001.
Ruud, P., An Introduction to Classical Econometric Theory. Oxford University Press, 2000.
Wooldridge, J. M., Introductory Econometrics. A Modern Approach. Thomson South-Western, 2009.
Wooldridge, J. M., Econometric Analysis of Cross Section and Panel Data. The MIT Press, 2010.
TARGET GROUP / REQUIREMENTS
Requirements for this course is the course content of Methods of Econometrics.
GRADING
The overall grade of the course is determined by the written exam, a learning objectives test and the exercises presented in the tutorials. Passing the written exam and an overall grade no lower than 4.0 are required to pass the course.
All details of the relevant regulations are described in the overview (external link, opens in a new window).
