Bootstrap-based bias correction and inference for dynamic panels with fixed effects

In this article, we describe a new command, xtbcfe, that performs the iterative bootstrap-based bias correction for the fixed-effects estimator in dynamic panels proposed by Everaert and Pozzi (2007, Journal of Economic Dynamics and Control 31: 1160-1184). We first simplify the core of their algorithm by 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 heteroskedasticity 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 command with an empirical example estimating a dynamic labor demand function.