HSG_Logo_EN_RGB

Stefan Sperlich

Course location

Home university

University of Geneva

Course location

Home university

University of Geneva
Sperlich
Stefan Sperlich made his diploma in mathematics at the University of Göttingen and holds a PhD in economics from the Humboldt University of Berlin. From 1998 to 2006 he was Professor for statistics at the University Carlos III de Madrid, from 2006 to 2010 chair of econometrics at the University of Göttingen, and is since 2010 professor for statistics and econometrics at the University of Geneva. His research interests are ranging from nonparametric statistics over small area statistics to empirical economics, in particular impact evaluation methods. He has been working since about 15 years as consultant for regional, national and international institutions, participated in development programs like EUROSociAL or UN assessment reports, is cofounder of the research center ‘Poverty, Equity and Growth in Developing Countries’ at the University of Göttingen, and research fellow at the Center for Evaluation and Development (Mannheim, Germany). He published in various top ranked scientific journals of different fields and was awarded in 2002 with the Koopmans econometric theory prize.

Courses taught by this instructor

Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

Next course

Location

Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

Location

Next course

Smart Data-Driven Econometrics

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.
...

...

A

Smart Data-Driven Econometrics

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.
...

...