Publication Type

Journal Article

Publication Date

2007

Abstract

Maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size and large cross section sample size asymptotics. This paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference, shows unbiasedness and analyzes efficiency. Monte Carlo studies show that our procedure achieves substantial bias reductions with only mild increases in variance, thereby substantially reducing root mean square errors. The method is compared with certain consistent estimators and is shown to have superior finite sample properties to the generalized method of moment (GMM) and the bias-corrected ML estimator.

Keywords

Autoregression, Bias reduction, Dynamic panel, Fixed effects, Indirect inference

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

157

Issue

1

First Page

68

Last Page

77

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2009.10.024

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1016/j.jeconom.2009.10.024

Included in

Econometrics Commons

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