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Tests for independence between categorical variables

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  • Sentana, Juan

Abstract

I prove the numerical equivalence between Pearson’s independence test statistic for categorical variables and the Lagrange Multiplier and overidentifying restrictions test statistics in several popular linear and non-linear regression models. I also show that its asymptotically equivalent Likelihood Ratio test is numerically identical in the non-linear regression models, and that the heteroskedasticity-robust Wald test statistic in the multivariate linear probability model and the moment condition model coincide with the Wald test statistic in the conditional multinomial model. Finally, I show that all these equivalences also apply to serial independence tests in discrete Markov chains.

Suggested Citation

  • Sentana, Juan, 2022. "Tests for independence between categorical variables," Economics Letters, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:ecolet:v:220:y:2022:i:c:s016517652200324x
    DOI: 10.1016/j.econlet.2022.110850
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    References listed on IDEAS

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    Cited by:

    1. Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2022. "Specification tests for non-Gaussian structural vector autoregressions," Working Papers wp2022_2212, CEMFI.

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    More about this item

    Keywords

    Linear Probability Model; Logit; Overidentifying Restrictions; Probit;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions

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