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Kernel-weighted specification testing under general distributions

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  • Sid Kankanala
  • Victoria Zinde-Walsh

Abstract

Kernel-weighted test statistics have been widely used in a variety of settings including non-stationary regression, inference on propensity score and panel data models. We develop the limit theory for a kernel-based specification test of a parametric conditional mean when the law of the regressors may not be absolutely continuous to the Lebesgue measure and is contaminated with singular components. This result is of independent interest and may be useful in other applications that utilize kernel smoothed U-statistics. Simulations illustrate the non-trivial impact of the distribution of the conditioning variables on the power properties of the test statistic.

Suggested Citation

  • Sid Kankanala & Victoria Zinde-Walsh, 2022. "Kernel-weighted specification testing under general distributions," Papers 2204.01683, arXiv.org, revised May 2023.
    • Handle: RePEc:arx:papers:2204.01683
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    File URL: http://arxiv.org/pdf/2204.01683
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    Cited by:

    1. Harold D Chiang & Yuya Sasaki, 2023. "On Using The Two-Way Cluster-Robust Standard Errors," Papers 2301.13775, arXiv.org.

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