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Conditional Distribution Model Specification Testing Using Chi-Square Goodness-of-Fit Tests

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  • Miguel A. Delgado
  • Julius Vainora

Abstract

This paper introduces chi-square goodness-of-fit tests to check for conditional distribution model specification. The data is cross-classified according to the Rosenblatt transform of the dependent variable and the explanatory variables, resulting in a contingency table with expected joint frequencies equal to the product of the row and column marginals, which are independent of the model parameters. The test statistics assess whether the difference between observed and expected frequencies is due to chance. We propose three types of test statistics: the classical trinity of tests based on the likelihood of grouped data, and two statistics based on the efficient raw data estimator -- namely, a Chernoff-Lehmann and a generalized Wald statistic. The asymptotic distribution of these statistics is invariant to sample-dependent partitions. Monte Carlo experiments demonstrate the good performance of the proposed tests.

Suggested Citation

  • Miguel A. Delgado & Julius Vainora, 2022. "Conditional Distribution Model Specification Testing Using Chi-Square Goodness-of-Fit Tests," Papers 2210.00624, arXiv.org, revised Sep 2023.
    • Handle: RePEc:arx:papers:2210.00624
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    References listed on IDEAS

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    1. Robinson, Peter M, 1988. "The Stochastic Difference between Econometric Statistics," Econometrica, Econometric Society, vol. 56(3), pages 531-548, May.
    2. Horowitz, Joel L., 1985. "Testing probabilistic discrete choice models of travel demand by comparing predicted and observed aggregate choice shares," Transportation Research Part B: Methodological, Elsevier, vol. 19(1), pages 17-38, February.
    3. Delgado, Miguel A. & Stute, Winfried, 2008. "Distribution-free specification tests of conditional models," Journal of Econometrics, Elsevier, vol. 143(1), pages 37-55, March.
    4. Heckman, James J, 1984. "The x[superscript]2 Goodness of Fit Statistic for Models with Parameters Estimated from Microdata," Econometrica, Econometric Society, vol. 52(6), pages 1543-1547, November.
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