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Empirical Economics

Erschienen in:

05.01.2021

A bias-corrected fixed effects estimator in the dynamic panel data model

verfasst von: Chihwa Kao, Long Liu, Rui Sun

Erschienen in: Empirical Economics | Ausgabe 1/2021

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Abstract

In this paper, we propose a biased-corrected FE estimator for the dynamic panel data model that works for the autoregressive coefficient \(\rho \in (-1,1]\). We further derive the asymptotic result of the suggested bias-corrected FE estimator. We show that when \(\rho =1\), the suggested estimator is super-consistent and is more efficient than the existing estimators that also work for \(\rho \in (-1,1]\). In addition, when the initial condition is nonstationary, many of the existing dynamic estimators become inconsistent; however, the consistency of the bias-corrected FE estimator we propose does not depend on the stationarity of the initial condition. We also compare the finite sample performances of these estimators using Monte Carlo simulations.

Vorheriger Artikel Cross-sectional quasi-maximum likelihood and bias-corrected pooled least squares estimators for short dynamic panels

Nächster Artikel Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada

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This result for the case \(\rho =1\) has also been derive in Theorem 2 in Kao (1999), which discusses the spurious panel data model.

We would thank the editor for pointing this out.

We would thank a referee for pointing this out.

It is easy to show that \({\hat{\rho }}_\mathrm{HP}=2{\hat{\rho }}_\mathrm{FD}+1\) and \({\hat{\rho }}_\mathrm{FD} -\rho \overset{p}{\rightarrow }-\frac{1+\rho }{2}\) as \((n,T)\rightarrow \infty \). Hence \({\hat{\rho }}_\mathrm{HP}\overset{p}{\rightarrow }\rho \) as \((n,T)\rightarrow \infty \).

We also checked performance of FEBC using \(\delta =\left( 0.05,0.1,0.15,0.2\right) \). These Monte Carlo simulation results are available upon request. These values of \(\delta \) only affect the cases when true value of \(\rho =0.9\) or 1. When T is 10 or 20, using a large \(\delta \), such as 0.2, reduces RMSE of the FEBC estimator to almost 0 when \(\rho =1\), while ends up with a relatively large RMSE when \(\rho =0.9\). On the other hand, using a small \(\delta \), such as 0.05, ends up with a relative larger RMSE of the FEBC estimator when \(\rho =1\), while a relatively small RMSE when \(\rho =0.9\). This finding is consistent with the discussion in Sect. 2. When T is large, the results of FEBC become robust to these different values of \(\delta \).

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Titel: A bias-corrected fixed effects estimator in the dynamic panel data model
verfasst von: Chihwa Kao
Long Liu
Rui Sun
Publikationsdatum: 05.01.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 1/2021
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-020-01995-0

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