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A New Form of the Information Matrix Test

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Author Info

  • Russell Davidson
  • James G. MacKinnon

Abstract

We develop a new form of the information matrix test for a wide variety of statistical models, and present full details for the special case of univariate nonlinear regression models. Chesher (1984) showed that the implicit alternative of the information matrix test is a model with random parameter variation. We exploit this fact by constructing the test against an explicit alternative of this type. The new test is computed using a double-length artificial regression, instead of the more conventional outer product of the gradient regression, which, although easy to use, is known to give test statistics with distributions very far from the asymptotic nominal distribution even in rather large samples. The new form on the other hand performs remarkably well, at least in the context of regression models. Some approximate finite-sample distributions are calculated and lend support to the use of the new form of the test.

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Bibliographic Info

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 724.

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Length: 30 pages
Date of creation: 1988
Date of revision:
Publication status: Published in Econometrica, 60, 1992
Handle: RePEc:qed:wpaper:724

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Web page: http://qed.econ.queensu.ca/
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Related research

Keywords: information matrix; IM test; double-length regression; DLR;

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Citations

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Cited by:
  1. BONTEMPS, Christian & MEDDAHI, Nour, 2002. "Testing Normality : A GMM Approach," Cahiers de recherche 2002-14, Universite de Montreal, Departement de sciences economiques.
  2. Joachim Zietz, 2006. "Detecting neglected parameter heterogeneity with Chow tests," Applied Economics Letters, Taylor & Francis Journals, vol. 13(6), pages 369-374.
  3. Dirk Hoorelbeke, 2004. "Bootstrap correcting the score test," Econometric Society 2004 North American Summer Meetings 228, Econometric Society.
  4. Stomberg, Christopher & White, Halbert, 2000. "Bootstrapping the Information Matrix Test," University of California at San Diego, Economics Working Paper Series qt158451cr, Department of Economics, UC San Diego.
  5. Wanling Huang & Artem Prokhorov, 2010. "A Goodness-of-fit Test for Copulas," Working Papers 10002, Concordia University, Department of Economics, revised Apr 2010.
  6. Russell Davidson & James G. MacKinnon, 1999. "Artificial Regressions," Working Papers 978, Queen's University, Department of Economics.
  7. Francisco Cribari-Neto, 1996. "On the Corrections to Information Matrix Tests," Econometrics 9601001, EconWPA.
  8. Teodosio Perez Amaral, 1994. "Una aplicación de los contrastes M y de la matriz de información dinámica: el caso de la demanda de dinero norteamericana 1960-1984," Investigaciones Economicas, Fundación SEPI, vol. 18(1), pages 193-201, January.
  9. Russell Davidson & James G. MacKinnon, 1994. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," Working Papers 903, Queen's University, Department of Economics.
  10. Geert Dhaene & Dirk Hoorelbeke, 2002. "The Information Matrix Test with Bootstrap-Based Covariance Matrix Estimation," Center for Economic Studies - Discussion papers ces0211, Katholieke Universiteit Leuven, Centrum voor Economische Studiën.
  11. Kaiser, Ulrich & Spitz, Alexandra, 2000. "Quantification of qualitative data using ordered probit models with an application to a business survey in the German service sector," ZEW Discussion Papers 00-58, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  12. Chesher, Andrew & Dumangane, Montezuma & Smith, Richard J., 2002. "Duration response measurement error," Journal of Econometrics, Elsevier, vol. 111(2), pages 169-194, December.
  13. Riccardo Lucchetti & Claudia Pigini, 2013. "A test for bivariate normality with applications in microeconometric models," Statistical Methods and Applications, Springer, vol. 22(4), pages 535-572, November.

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