A new diagnostic test for cross-section independence in nonlinear panel data models
Date
2009
Authors
Chen, Jia
Gao, Jiti
Li, Degui
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of ESAM09, 2009
Statement of Responsibility
Jia Chen, Jiti Gao and Degui Li
Conference Name
Australasian Meeting of the Econometric Society (2009 : Canberra, Australia)
ESAM09
ESAM09
Abstract
In this paper, we propose a new diagnostic test for residual cross section in dependence
in a nonparametric panel data model. The proposed test is a nonparametric counterpart
of an existing test proposed in Pesaren (2004) for the parametric case. First of all, we
establish an asymptotic distribution of the proposed test statistic under the null
hypothesis. As shown in the parametric case, the asymptotic distribution is a standard
normality. We then analyze the power function of the proposed test statistic under an
alternative hypothesis that involves a nonlinear multi-factor model. In order to compute
both the sizes and the power values, we select a simulated critical value in each case
based on a simple bootstrap simulation scheme in the context of nonparametric panel
data models. We finally provide several numerical examples and an empirical analysis
of a set of CPI data in Australian capital cities to illustrate the finite sample performance
of the proposed test statistic.
School/Discipline
School of Economics