BOOTSTRAP-BASED BIAS CORRECTION AND INFERENCE FOR DYNAMIC PANELS WITH FIXED EFFECTS

This article describes a new Stata routine, xtbcfe, that performs the iterative bootstrap-based bias correction for the fixed effects (FE) estimator in dynamic panels proposed by Everaert and Pozzi (Journal of Economic Dynamics and Control, 2007). We first simplify the core of their algorithm using the invariance principle and subsequently extend it to allow for unbalanced and higher order dynamic panels. We implement various bootstrap error resampling schemes to account for general heteroscedasticity and contemporaneous cross-sectional dependence. Inference can be performed using a bootstrapped variance-covariance matrix or percentile intervals. Monte Carlo simulations show that the simplification of the original algorithm results in a further bias reduction for very small T. The Monte Carlo results also support the bootstrap-based bias correction in higher order dynamic panels and panels with cross-sectional dependence. We illustrate the routine with an empirical example estimating a dynamic labour demand function.