Bias Reduction in Dynamic Panel Data Models by Common Recursive Mean Adjustment

37 Pages Posted: 16 Jul 2009

See all articles by C. Y. Choi

C. Y. Choi

University of Texas at Arlington - College of Business Administration - Department of Economics

Nelson C. Mark

University of Notre Dame - Department of Economics and Econometrics; National Bureau of Economic Research (NBER)

Donggyu Sul

Independent

Date Written: May 25, 2009

Abstract

The within-group estimator (same as the least squares dummy variable estimator) of the dominant root in dynamic panel regression is known to be biased downwards. This paper studies recursive mean adjustment (RMA) as a strategy to reduce this bias for AR(p) processes that may exhibit cross-sectional dependence. Asymptotic properties for N, T → ∞ jointly are developed. When (log2 T)(N/T ) → ζ where ζ is a non-zero constant, the estimator exhibits nearly negligible inconsistency. Simulation experiments demonstrate that the RMA estimator performs well in terms of reducing bias, variance and mean square error both when error terms are cross-sectionally independent and when they are not. RMA dominates comparable estimators when T is small and/or when the underlying process is persistent.

Keywords: Recursive Mean Adjustment, Fixed Effects, Cross-sectional Dependence.

JEL Classification: C33

Suggested Citation

Choi, Chi-Young and Mark, Nelson Chung and Sul, Donggyu, Bias Reduction in Dynamic Panel Data Models by Common Recursive Mean Adjustment (May 25, 2009). Available at SSRN: https://ssrn.com/abstract=1434143 or http://dx.doi.org/10.2139/ssrn.1434143

Chi-Young Choi

University of Texas at Arlington - College of Business Administration - Department of Economics ( email )

Box 19479 UTA
Arlington, TX 76019
United States

Nelson Chung Mark

University of Notre Dame - Department of Economics and Econometrics ( email )

442 Flanner
Notre Dame, IN 46556
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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