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Instructors: M.Sc. Phillip Heiler
Event type:
Seminar
Org-unit: Corporate Management & Economics
Displayed in timetable as:
Quant. Methods II
Hours per week:
3
Credits:
6,0
Note: In your exam regulations, differing credits may have been specified.
Location:
Campus der Zeppelin Universität
Language of instruction:
Englisch
Min. | Max. participants:
10 | 35
Priority scheme: Standard-Priorisierung
Course content:
This lecture comprises a selection of empirically relevant methods from econometrics and statistics. The methods are discussed in the broader context of statistical decision theory and model selection. Empirical and theoretical examples serve as enhancement for understanding the practical relevance and implementation of the methods. The core areas are:
- Nonparametric Econometrics
- Model Selection and Statistical Learning
The first area introduces non- and semiparametric estimation techniques for densities and regression models. Additionally, the analytical tools for evaluating the statistical properties are discussed. Area two covers different methods for model selection from statistics and econometrics. It ranges from statistical decision theory and classical model selection to modern methods from the econometric and statistical/machine learning literature including methods for high-dimensional data sets such as regularization, shrinkage estimation, model averaging and boosting. Software packages in R are are used for illustration.
Educational objective:
The course empowers the students to address fundamental questions regarding the selection of statistical or econometric models in empirical research from a broader perspective. They are able to discuss and evaluate the appropriateness of methods and corresponding assumptions in a variety of contexts. The analytical and empirical tools can directly be used for empirical practice and fundamentally ease the understanding of the literature about and beyond the areas discussed.
Further information about the exams:
The final grade comprises of two components:
- Presentation work of an assigned topic (50%)
- Written Exam (50%)
Mandatory literature:
Li, Q. and J.S. Racine (2007): Nonparametric Econometrics. Princeton University Press.
T. Hastie, R. Tibshirani, J. Friedman (2009): The Elements of Statistical Learning. Springer.
P. Bühlmann, S. van de Geer (2011): Statistics for High-Dimensional Data. Springer
T. Hastie, R. Tibshirani, M. Wainwright (2015): Statistical Learning with Sparsity. CRC Press.
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