Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Model
AbstractIn 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.
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Bibliographic InfoPaper provided by Netherlands Central Bank, Research Department in its series DNB Working Papers with number 140.
Date of creation: Jun 2007
Date of revision:
Cross-section dependence; nonlinear panel data model.;
Other versions of this item:
- Cheng Hsiao & M. Hashem Pesaran & Andreas Pick, 2007. "Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models," CESifo Working Paper Series 1984, CESifo Group Munich.
- Hsiao, Cheng & Pesaran, M. Hashem & Pick, Andreas, 2007. "Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models," IZA Discussion Papers 2756, Institute for the Study of Labor (IZA).
- Hsiao, C. & Pesaran, M.H. & Pick, A., 2007. "Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models," Cambridge Working Papers in Economics 0716, Faculty of Economics, University of Cambridge.
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- G3 - Financial Economics - - Corporate Finance and Governance
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-08-27 (All new papers)
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