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.

Governance by targets and the performance of cross‐sector partnerships: Do partner diversity and partnership capabilities matter?

“…Our results imply that a more balanced representation of partners from different sectors reduces the transaction costs associated with achieving comprehensive action in pursuit of social goals (Lasker et al, 2001). Partnership experience and administrative capacity too may reduce those costs, 14 Unlike Kiviet's (1995) procedure, Everaert and Pozzi's (2007) bootstrapping procedure avoids relying on a strict set of assumptions, such as homoscedasticity (De Vos, Everaert, & Ruyssen, 2015).…”

“…Unlike Kiviet's () procedure, Everaert and Pozzi's () bootstrapping procedure avoids relying on a strict set of assumptions, such as homoscedasticity (De Vos, Everaert, & Ruyssen, ).…”

Learning from failure in healthcare: Dynamic panel evidence of a physician shock effect

“…To identify such learning from failure effects, we apply the recently developed bias-corrected FE estimator by De Vos et al (2015) and the split-panel jackknife estimator proposed by Dhaene and Jochmans (2015) to address the econometric challenges inherent to nonlinear dynamic panel data settings. In this paper, we shed light on the question how procedural failures of physicians (or teams) affect subsequent patient outcomes.…”

“…Because of the incidental parameters problem, the FE estimates are inconsistently estimated, and this carries over into inconsistency for all other coefficients (Baltagi, 2005;Wooldridge, 2010). We refrain from applying GMM-type estimators as they regularly suffer from poor small sample properties due to weak instrument problems (see Pua, 2015;De Vos et al, 2015;Bruno, 2005). To address these endogeneity issues, difference and system GMM-type estimators are commonly used in the applied literature.…”

“…As a remedy to the incidental parameter problem and the Nickell bias, we apply the bias-corrected FE estimator proposed by De Vos, Everaert, and Ruyssen (2015) which has shown to have superior small sample properties compared with classical generalized method of moments (GMM)-type estimators and FE linear probability models (LPMs). In addition to the bias-correction approach, we estimate the learning from failure effects using the split-panel jackknife FE estimator recently proposed by Dhaene and Jochmans (2015).…”