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

Ignace De Vos, Gerdie Everaert and Ilse Ruyssen

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: 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.

Keywords: st0001; xtbcfe; bootstrap-based bias correction; dynamic panel data; unbalanced; higher order; heteroscedasticity; cross-sectional dependence; Monte Carlo; labour demand (search for similar items in EconPapers)
Pages: 32 pages
Date: 2015-04
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (63)

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Related works:
Journal Article: Bootstrap-based bias correction and inference for dynamic panels with fixed effects (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:rug:rugwps:15/906

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