Bias Reduction in Nonlinear and Dynamic Panels in the Presence of Cross-Section Dependence

59 Pages Posted: 12 Dec 2012 Last revised: 16 May 2019

See all articles by Cavit Pakel

Cavit Pakel

Bilkent University - Department of Economics

Date Written: May 16, 2019

Abstract

Fixed effects estimation of nonlinear dynamic panel models is subject to the incidental parameter issue, leading to a biased asymptotic distribution. While this problem has been studied extensively in the literature, a general analysis allowing for both serial and cross-sectional dependence is missing. In this paper we investigate the large-N,T theory of the profile and integrated likelihood estimators, allowing for dependence across both dimensions. We show that under stronger dependence types the asymptotic bias disappears, but a O_p(1/T) small-sample bias remains. We provide bias correction and inference methods, and also obtain primitive conditions for asymptotic normality under various dependence settings.

Keywords: Nonlinear and dynamic panels, incidental parameter bias, integrated likelihood method, profile likelihood method, female labour force participation

JEL Classification: C13, C23

Suggested Citation

Pakel, Cavit, Bias Reduction in Nonlinear and Dynamic Panels in the Presence of Cross-Section Dependence (May 16, 2019). Available at SSRN: https://ssrn.com/abstract=2157212 or http://dx.doi.org/10.2139/ssrn.2157212

Cavit Pakel (Contact Author)

Bilkent University - Department of Economics ( email )

Ankara, 06800
Turkey

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