Template-Type: ReDIF-Paper 1.0 Author-Name: Johannes S. Kunz Author-Email: johannes.kunz@monash.edu Author-Workplace-Name: Monash University Author-Name: Kevin E. Staub Author-Email: kevin.staub@unimelb.edu.au Author-Workplace-Name: University of Melbourne Author-Name: Rainer Winkelmann Author-Email: rainer.winkelmann@econ.uzh.ch Author-Workplace-Name: University of Zurich Title: Predicting Individual Effects in Fixed Effects Panel Probit Models Abstract: Many applied settings in empirical economics require estimation of a large number of individual effects, like teacher effects or location effects; in health economics, prominent examples include patient effects, doctor effects, or hospital effects. Increasingly, these effects are the object of interest of the estimation, and predicted effects are often used for further descriptive and regression analyses. To avoid imposing distributional assumptions on these effects, they are typically estimated via fixed effects methods. In short panels, the conventional maximum likelihood estimator for fixed effects binary response models provides poor estimates of these individual effects since the finite sample bias is typically substantial. We present a bias-reduced fixed effects estimator that provides better estimates of the individual effects in these models by removing the first-order asymptotic bias. An additional, practical advantage of the estimator is that it provides finite predictions for all individual effects in the sample, including those for which the corresponding dependent variable has identical outcomes in all time periods over time (either all zeros or ones); for these, the maximum likelihood prediction is infinite. We illustrate the approach in simulation experiments and in an application to health care utilization. Stata estimation command is available at [Github:brfeglm](https://github.com/JohannesSKunz/brfeglm) Creation-Date: 2021-05 File-URL: http://soda-wps.s3-website-ap-southeast-2.amazonaws.com/RePEc/ajr/sodwps/2021-05.pdf File-Format: Application/pdf Number: 2021-05 Classification-JEL: C23, C25, I11, I18 Keywords: Incidental parameter bias, Perfect prediction, Fixed effects, Panel data, Bias reduction Handle: RePEc:ajr:sodwps:2021-05