Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models

Cheng Hsiao, M Hashem Pesaran () and Andreas Pick ()
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Andreas Pick: CIMF, Cambridge University

No 2756, IZA Discussion Papers from Institute for the Study of Labor (IZA)

Abstract: In this paper we discuss tests for residual cross section dependence in nonlinear panel data models. The tests are based on average pair-wise residual correlation coefficients. In nonlinear models, the definition of the residual is ambiguous and we consider two approaches: deviations of the observed dependent variable from its expected value and generalized residuals. We show the asymptotic consistency of the cross section dependence (CD) test of Pesaran (2004). In Monte Carlo experiments it emerges that the CD test has the correct size for any combination of N and T whereas the LM test relies on T large relative to N. We then analyze the roll-call votes of the 104th U.S. Congress and find considerable dependence between the votes of the members of Congress.

Keywords: cross-section dependence; nonlinear panel data model (search for similar items in EconPapers)
JEL-codes: C12 C33 C35 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2007-04

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Related works:
Working Paper: Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Model (2007)
Working Paper: Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models (2007)
Working Paper: Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models (2007)
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