Publication Type

Working Paper

Publication Date

2006

Abstract

It is well-known that maximum likelihood (ML) estimation of the autoregres- sive parameter of a dynamic panel data model with …xed e¤ects is inconsistent under …xed time series sample size (T) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in practical applications when T is small and the autoregressive parameter is close to unity. The present paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference (Gouriéroux et al., 1993), shows unbiasedness and analyzes e¢ ciency. The method is implemented in a simple linear dynamic panel model, but has wider applicability and can, for instance, be easily ex- tended to more complicated frameworks such as nonlinear models. Monte Carlo studies show that the proposed 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 bias-corrected ML estimators previously proposed in the literature and is shown to have superior …nite sample properties to GMM and the bias-corrected ML of Hahn and Kuersteiner (2002). Finite sample performance is compared with that of a recent estimator proposed by Han and Phillips (2005

Keywords

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

Discipline

Econometrics

Research Areas

Econometrics

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.

Included in

Econometrics Commons

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