Bootstrap-based bias correction and inference for dynamic panels with fixed effects
Abstract. 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 higherorder 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.
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Ignace De Vos, Gerdie Everaert, Ilse Ruyssen
View all articles with these keywords:
xtbcfe, bootstrap-based bias correction, dynamic panel data, unbalanced, higher order, heteroskedasticity, cross-sectional dependence, Monte Carlo, labor demand, bootstrap
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