Structured Additive Distributional Regression

• Applicant: Professor Dr. Thomas Kneib
• Subject Area: Statistics and Econometrics
• Term: from 2010 to 2016
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 166547046

Regression models form one of the standard tools for empirical analyses in all scientific disciplines and in particular in economics. While usual regression specifications such as the linear model or generalized linear models aim at describing the expectation of a response conditional on covariates, recent interest has shifted towards models that allow to analyse more general properties of the distribution of a response (we will refer to this as distributional regression in the following). This comprises completely distribution free approaches such as quantile and expectile regression as well as flexible parametric approaches for location, scale and shape. The increasing availability of complex covariate information also induces a requirement for similarly complex predictor specifications such as nonlinear and spatial effects in the context of distributional regression.This project extends different classes of distributional regression and develops corresponding inferential techniques. The model classes considered comprise different versions of quantile and expectile regression, modal regression and regression models for location, scale and shape. The developed methods will furthermore be employed in the different areas of application for empirical analyses.

• DFG Programme: Research Grants
• Participating Person: Dr. Fabian Otto-Sobotka