A New Diagnostic Test for Cross-Section Independence in Nonparametric Panel Data Model
AbstractIn this paper, we propose a new diagnostic test for residual crossÂ–section independence in a nonparametric panel data model. The proposed nonparametric crossÂ–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 multiÂ–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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by University of Adelaide, School of Economics in its series School of Economics Working Papers with number 2009-16.
Length: 36 pages
Date of creation: 2009
Date of revision:
CrossÂ–section independence; local linear smoother; nonlinear panel data model; nonparametric diagnostic test; size and power function;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Vasilis Sarafidis & Tom Wansbeek, 2012.
"Cross-Sectional Dependence in Panel Data Analysis,"
Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
- 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.
- 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.
- Chen, Jia & Gao, Jiti & Li, Degui, 2012. "Semiparametric trending panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 171(1), pages 71-85.
- Jia Chen & Jiti Gao & Degui Li, 2011. "Semiparametric Trending Panel Data Models with Cross-Sectional Dependence," Monash Econometrics and Business Statistics Working Papers 15/11, Monash University, Department of Econometrics and Business Statistics.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dmitriy Kvasov).
If references are entirely missing, you can add them using this form.