Publication Type
Journal Article
Version
publishedVersion
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
Citation
Gourieroux, Christian; Phillips, Peter C. B.; and YU, Jun.
Indirect Inference for Dynamic Panel Models. (2007). Journal of Econometrics. 157, (1), 68-77.
Available at: https://ink.library.smu.edu.sg/soe_research/279
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2009.10.024