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A NEW DIAGNOSTIC TEST FOR CROSS-SECTION UNCORRELATEDNESS IN NONPARAMETRIC PANEL DATA MODELS

Published online by Cambridge University Press:  27 April 2012

Jia Chen*
Affiliation:
Monash University and University of Queensland
Jiti Gao
Affiliation:
Monash University and University of Adelaide
Degui Li
Affiliation:
Monash University
*
*Address correspondence to Jia Chen, School of Mathematics, University of Queensland, ST Lucia, Brisbane 4072, Australia; e-mail: jiachen1981@gmail.com.

Abstract

In this paper, we propose a new diagnostic test for residual cross-section uncorrelatedness (CU) in a nonparametric panel data model. The proposed nonparametric CU test is a nonparametric counterpart of an existing parametric cross-section dependence test proposed in Pesaran (2004, Cambridge Working paper in Economics 0435). Without assuming cross-section independence, we establish asymptotic distribution for the proposed test statistic for the case where both the cross-section dimension and the time dimension go to infinity simultaneously, and then analyze the power function of the proposed test under a sequence of local alternatives that involve a nonlinear multifactor model. The simulation results and real data analysis show that the nonparametric CU test associated with an asymptotic critical value works well.

Type
Miscellanea
Copyright
Copyright © Cambridge University Press 2012

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References

Breusch, T.S. & Pagan, A.R. (1980) The Lagrange multiplier test and its application to model specifications in econometrics. Review of Economic Studies 47, 239253.CrossRefGoogle Scholar
Cai, Z. & Li, Q. (2008) Nonparametric estimation of varying coefficient dynamic panel data models. Econometric Theory 24, 13211342.CrossRefGoogle Scholar
Chen, J., Gao, J., & Li, D. (2009) Testing for Cross-Section Uncorrelatedness in Nonparametric Panel Data Models: Theory and Practice. Working paper, University of Adelaide. Available athttp://www.adelaide.edu.au/directory/jiti.gao.
Fan, J. & Gijbels, J. (1996) Local Polynomial Modelling and Its Applications. Chapman and Hall.Google Scholar
Fan, J. & Yao, Q. (2003) Nonlinear Time Series: Nonparametric and Parametric Methods. Springer-Verlag.CrossRefGoogle Scholar
Frees, E.W. (1995) Assessing cross sectional correlation in panel data. Journal of Econometrics 69, 393414.CrossRefGoogle Scholar
Gao, J. (2007) Nonlinear Time Series: Semiparametric and Nonparametric Methods. Chapman and Hall.CrossRefGoogle Scholar
Hsiao, C., Pesaran, M.H., & Pick, A. (2007) Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models. IZA Discussion paper 2756.
Huang, H., Kab, C., & Urga, G. (2008) Copula-based tests for cross-sectional independence in panel models. Economics Letters 100, 224228.CrossRefGoogle Scholar
Li, Q. & Racine, J. (2007) Nonparametric Econometrics: Theory and Practice. Princeton University Press.Google Scholar
Ng, S. (2006) Testing cross section correlation in panel data using spacing. Journal of Business & Economic Statistics 24, 1223.CrossRefGoogle Scholar
Pesaran, M.H. (2004) General Diagnostic Tests for Cross Section Dependence in Panels. Cambridge Working paper in Economics 0435.
Pesaran, M.H., Ullah, A., & Yamagata, T. (2008) A bias adjusted LM test of error cross section independence. Econometrics Journal 11, 105127.CrossRefGoogle Scholar
Phillips, P.C.B. & Moon, H. (1999) Linear regression limit theory for nonstationary panel data. Econometrica 67, 10571111.CrossRefGoogle Scholar
Sarafidis, V., Yamagata, T., & Robertson, D. (2009) A test of cross section dependence for a linear dynamic panel model with regressors. Journal of Econometrics 148, 149161.CrossRefGoogle Scholar
Shao, Q. & Yu, H. (1996) Weak convergence for weighted empirical processes of dependent sequences. Annals of Probability 24, 20982127.Google Scholar
Su, L. & Ullah, A. (2009) Testing conditional uncorrelatednesss. Journal of Business & Economic Statistics 27, 1829.CrossRefGoogle Scholar
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A NEW DIAGNOSTIC TEST FOR CROSS-SECTION UNCORRELATEDNESS IN NONPARAMETRIC PANEL DATA MODELS
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