"Grouped fixed effects regularization for binary choice models" by Francesco Valentini

Topic

We study the application of the Grouped Fixed Effects (GFE) estimator to binary choice models for panel data. This approach discretizes unobserved heterogeneity via k-means clustering and performs maximum likelihood estimation, reducing the number of fixed effects in finite samples. This regularization helps analyze rare events by mitigating complete separation, which lead to data loss and imprecise estimation of average partial effects. We focus on dynamic models with few state transitions. The effectiveness of this method is demonstrated through simulations and real data applications.