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.
Autoregression, Bias reduction, Dynamic panel, Fixed effects, Indirect inference
Journal of Econometrics
Gourieroux, Christian; Phillips, Peter C. B.; and YU, Jun.
Indirect Inference for Dynamic Panel Models. (2007). Journal of Econometrics. 157, (1), 68-77. Research Collection School Of Economics.
Available at: http://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 License.