Applied Empirical Modeling of Nonlinearity and Endogeneity in Regression Models (WS 2020/21)
The course is given by Prof. Dr. Richard T. Gretz.
It is unsure if this course can be delivered.
The course will presumably take place digitally in March 2021; we are currently talking to the MCM guest lecturers (including Prof. Dr. Houston) to find individual solutions accounting for the current development of Covid-19.
The application period will be announced soon.
Applications for this course will be possible for all doctoral students of the faculty of business and economics and minor research students by sending an email within the upcoming application period (t.b.a.) to Tanja Geringhoff (email@example.com).
Additional course information (e.g., the complete syllabus) and materials will be made available to the course participants via learnweb. The enrollment passphrase will be sent to the course participants via e-mail.
Information for students of the Minor Research: If your application was successful, please register at the examination office for the early examination period. The examination modalities will be published here once they have been finalized.
Information for all PhD students: The course is handled as an A certificate / research methods for the PhD program.
Often empirical problems do not fit the modeling assumptions of Ordinary Least Squares (OLS) estimation. This workshop looks at two specific scenarios: (1) Nonlinearities in dependent and independent variables and (2) instrumental variable techniques for dealing with endogeneity and non-random sample selection. These problems are often encountered in applied work. The goal of this workshop is to provide researchers with tools used to address some of the inadequacies of traditional OLS estimation in each setting.
We begin by looking at different nonlinear approaches to modeling discrete choice. We also extend the theme of nonlinearity to the independent variable side by discussing the interpretation of interaction effects in traditional OLS. Then we consider different instrumental variable strategies to deal with the problem of endogeneity. Finally, we combine the themes of nonlinearity and instrumental variables by considering selection models to deal with non-random samples.
By the end of the workshop, you should be able to understand and (equally important) run Stata code to model dichotomous dependent variables with logit and probit estimations, perform instrumental variable estimation and accompanying tests of instrument exogeneity and relevance, and estimate models controlling for selection bias.
- Ronny Behrens (accompanying)