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A New Diagnostic Test for Cross-Section Independence in Nonparametric Panel Data Model


Author Info

  • Jia Chen

    (School of Economics, University of Adelaide)

  • Jiti Gao

    (School of Economics, University of Adelaide)

  • Degui Li

    (School of Economics, University of Adelaide)


In this paper, we propose a new diagnostic test for residual cross–section independence in a nonparametric panel data model. The proposed nonparametric cross–section dependence (CD) test is a nonparametric counterpart of an existing parametric CD test proposed in Pesaren (2004) for the parametric case. We establish an asymptotic distribution of the proposed test statistic under the null hypothesis. As in the parametric case, the proposed test has an asymptotically normal distribution. We then analyze the power function of the proposed test under an alternative hypothesis that involves a nonlinear multi–factor model. We also provide several numerical examples. The small sample studies show that the nonparametric CD test associated with an asymptotic critical value works well numerically in each individual case. An empirical analysis of a set of CPI data in Australian capital cities is given to examine the applicability of the proposed nonparametric CD test.

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Bibliographic Info

Paper provided by University of Adelaide, School of Economics in its series School of Economics Working Papers with number 2009-16.

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Length: 36 pages
Date of creation: 2009
Date of revision:
Handle: RePEc:adl:wpaper:2009-16

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Keywords: Cross–section independence; local linear smoother; nonlinear panel data model; nonparametric diagnostic test; size and power function;


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Cited by:
  1. Jia Chen & Degui Li & Jiti Gao, 2013. "Non- and Semi-Parametric Panel Data Models: A Selective Review," Monash Econometrics and Business Statistics Working Papers 18/13, Monash University, Department of Econometrics and Business Statistics.
  2. Jia Chen & Jiti Gao & Degui Li, 2010. "Semiparametric Trending Panel Data Models with Cross-Sectional Dependence," School of Economics Working Papers 2010-10, University of Adelaide, School of Economics.
  3. G. Pan & J. Gao & Y. Yang & M. Guo, 2012. "Independence Test for High Dimensional Random Vectors," Monash Econometrics and Business Statistics Working Papers 1/12, Monash University, Department of Econometrics and Business Statistics.
  4. Sarafidis, Vasilis & Wansbeek, Tom, 2010. "Cross-sectional Dependence in Panel Data Analysis," MPRA Paper 20367, University Library of Munich, Germany.
  5. Gao, Jiti & Pan, Guangming & Yang, Yanrong, 2012. "Testing Independence for a Large Number of High–Dimensional Random Vectors," MPRA Paper 45073, University Library of Munich, Germany, revised 15 Mar 2013.


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