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Smart Data-Driven Econometrics

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Further and more detailed information, including the schedule, can be found in the current course tables in the syllabus of the respective course, if the course is offered in the next sessions. The following text serves as information on what can be expected in terms of content in the course.

The topic is estimation and testing of regression problems typically considered in microeconometrics by the means of (standard) nonparametric methods. The concept/content is: nonparametric density estimation (univariate, joint, conditional); nonparametric estimation of conditional moments; miscellaneous (model selection, bandwidth choice, conditional distribution); semiparametric estimation of generalized structured models; nonparametric testing. The approach is teaching half intuition, half (asymptotic) theory. After a successful completion, the students will know, understand and be able to apply nonparametric methods for data analysis, in particular estimation and regression. Moreover, the mixed approach enables them to broaden and deepen their knowledge in this direction for also applying non- and semiparametric methods in much more complex situations than those outlined in this course.