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Random Cell Chi-Square Diagnostic Tests for Econometric Models: I. Introduction and Applications

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Abstract

This paper and its sequel, Andrews [4], extend the Pearson chi-square testing method to non-dynamic parametric econometric models, in particular, models with covariates. The present paper introduced the test and discusses a wide variety of applications. Andrews [4] establishes the asymptotic properties of the test, by extending recent probabilistic results for the weak convergence of empirical processes indexed by sets. The chi-square test that is introduced can be used to test goodness-of-fit of a parametric model, as well as to test particular aspects of the parametric model that are of interest. In the event of rejection of the null hypothesis of correct specification, the test provides information concerning the direction of departure from the null. The results allow for estimation of the parameters of the model by quite general methods. The cells used to construct the test statistic my be random and can be specified in a general form.

Suggested Citation

  • Donald W.K. Andrews, 1985. "Random Cell Chi-Square Diagnostic Tests for Econometric Models: I. Introduction and Applications," Cowles Foundation Discussion Papers 762, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:762
    Note: CFP 698.
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    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d07/d0762.pdf
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    Cited by:

    1. Andrews, Donald W. K., 1987. "Asymptotic Results for Generalized Wald Tests," Econometric Theory, Cambridge University Press, vol. 3(3), pages 348-358, June.

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