Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models

Abstract

AbstractWe propose a multiple-step procedure to compute average partial effects (APEs) for fixed-effects static and dynamic logit models estimated by (pseudo) conditional maximum likelihood. As individual effects are eliminated by conditioning on suitable sufficient statistics, we propose evaluating the APEs at the maximum likelihood estimates for the unobserved heterogeneity, along with the fixed-T consistent estimator of the slope parameters, and then reducing the induced bias in the APEs by an analytical correction. The proposed estimator has bias of order $$O(T^{-2})$$ O ( T - 2 ) , it performs well in finite samples and, when the dynamic logit model is considered, better than alternative plug-in strategies based on bias-corrected estimates for the slopes, especially in panels with short T. We provide a real data application based on labour supply of married women.