Fixed Effects Estimation of Structural Parameters and Marginal Effects in Panel Probit Models

Fixed effects estimators of nonlinear panel models can be severely biased due to the incidental parameters problem. In this paper I find that the most important component of this incidental parameters bias for probit fixed effects estimators of index coefficients is proportional to the true value of these coe±cients, using a large-T expansion of the bias. This result allows me to derive a lower bound for this bias, and to show that fixed effects estimates of ratios of coefficients and average marginal effects have zero bias in the absence of heterogeneity, and have negligible bias relative to their true values for a wide variety of distributions of regressors and individual effects. New bias corrected estimators for index coefficients and marginal effects with improved finite sample properties are also proposed for static and dynamic probit, logit, and linear probability models with predetermined regressors.