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A Consistent Model Specification Test with Mixed Discrete and Continuous Data

Author

Listed:
  • Cheng Hsiao
  • Qi Li
  • Jeff Racine

Abstract

In this paper we propose a nonparametric kernel-based model specification test that can be used when the regression model contains both discrete and continuous regressors. We employ discrete variable kernel functions and we smooth both the discrete and continuous regressors using least squares cross-validation methods. The test statistic is shown to have an asymptotic normal null distribution. We also prove the validity of using the wild bootstrap method to approximate the null distribution of the test statistic, the bootstrap being our preferred method for obtaining the null distribution in practice. Simulations show that the proposed test has significant power advantages over conventional kernel tests which rely upon frequency-based nonparametric estimators that require sample splitting to handle the presence of discrete regressors.

Suggested Citation

  • Cheng Hsiao & Qi Li & Jeff Racine, 2006. "A Consistent Model Specification Test with Mixed Discrete and Continuous Data," IEPR Working Papers 06.47, Institute of Economic Policy Research (IEPR).
  • Handle: RePEc:scp:wpaper:06-47
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    References listed on IDEAS

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    More about this item

    Keywords

    Consistent test; parametric functional form; nonparametric estimation;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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