Partial Effects for Binary Outcome Models with Unobserved Heterogeneity

Abstract

Unobserved heterogeneity is ubiquitous in empirical research. In this article, I propose a method for estimating binary outcome models with panel data in the presence of unobserved heterogeneity, called the Penalized Flexible Correlated Random Effects (PF-CRE) estimator. I show that this estimator produces consistent and efficient estimates of the model parameters. PF-CRE also provides consistent estimates of partial effects, which cannot be calculated with existing consistent estimators. Using Monte Carlo simulations, I show that PF-CRE performs well in finite samples. I also illustrate the performance of PF-CRE in two real-data applications: a small T study on party contacts and tactical voting during the 2015 UK general election and a large T panel dealing with the effect of economic sanctions on government stability. In both cases I find that PF-CRE is a valid approach that reduces bias or generates efficiency gains in the estimation.