Often, empirical research problems do not fit the assumptions underlying Ordinary Least Squares (OLS) estimation. Applied researchers frequently encounter endogeneity, nonlinear relationships among regressors, and data structures involving repeated observations over time or across units. This workshop focuses on three core challenges commonly faced in applied empirical work: (1) endogeneity and instrumental variable methods, (2) nonlinearities on the right-hand side of regression models, and (3) panel data analysis. Each topic is motivated by practical empirical problems rather than abstract econometric theory. The course begins with an in-depth treatment of endogeneity, including its sources, consequences for estimation, and strategies for identification using instrumental variables. We then turn to nonlinearities in independent variables, with a focus on interaction effects, marginal effects, and interpretation challenges in regression models. Finally, the workshop introduces panel data methods, covering fixed effects, random effects, and related extensions. By the end of the workshop, participants should be able to understand and implement a range of applied econometric models using Stata, including instrumental variable estimation and diagnostics, models with interaction effects and nonlinearities, and panel data models addressing unobserved heterogeneity and selection concerns.

Kurs im HIS-LSF