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Mean Average Estimation of Dynamic Panel Models with Nonstationary Initial Condition

Essays in Honor of Peter C. B. Phillips

ISBN: 978-1-78441-183-1

ISSN: 0731-9053

Publication date: 21 November 2014

Abstract

This paper proposes a new class of estimators for the autoregressive coefficient of a dynamic panel data model with random individual effects and nonstationary initial condition. The new estimators we introduce are weighted averages of the well-known first difference (FD) GMM/IV estimator and the pooled ordinary least squares (POLS) estimator. The proposed procedure seeks to exploit the differing strengths of the FD GMM/IV estimator relative to the pooled OLS estimator. In particular, the latter is inconsistent in the stationary case but is consistent and asymptotically normal with a faster rate of convergence than the former when the underlying panel autoregressive process has a unit root. By averaging the two estimators in an appropriate way, we are able to construct a class of estimators which are consistent and asymptotically standard normal, when suitably standardized, in both the stationary and the unit root case. The results of our simulation study also show that our proposed estimator has favorable finite sample properties when compared to a number of existing estimators.

Keywords

Citation

Chao, J., Kim, M. and Sul, D. (2014), "Mean Average Estimation of Dynamic Panel Models with Nonstationary Initial Condition", Essays in Honor of Peter C. B. Phillips (Advances in Econometrics, Vol. 33), Emerald Group Publishing Limited, Bingley, pp. 241-279. https://doi.org/10.1108/S0731-905320140000033008

Publisher

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Emerald Group Publishing Limited

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