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

We propose a multiple-step procedure to compute average partial effects (APEs) for fixed-effects panel logit models estimated by Conditional Maximum Likelihood (CML). As individual effects are eliminated by conditioning on suitable sufficient statistics, we propose evaluating the APEs at the ML estimates for the unobserved heterogeneity, along with the fixed-T consistent estimator of the slope parameters, and then reducing the induced bias in the APE by an analytical correction. The proposed estimator has bias of order 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 with small n and T. We provide a real data application based on labour supply of married women.