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Adaptive testing using data-driven method selecting smoothing parameters

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  • Wang, Luya

Abstract

We consider the problem of selecting the smoothing parameter by a data-driven method in adaptive testing of a parametric model against a nonparametric alternative model. Simulations show that our proposed procedure works well and outperforms existing approaches. We discuss extensions of our method to more general model specification testing problems including testing a parametric quantile function and testing nonparametric significance.

Suggested Citation

  • Wang, Luya, 2022. "Adaptive testing using data-driven method selecting smoothing parameters," Economics Letters, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:ecolet:v:215:y:2022:i:c:s0165176522001495
    DOI: 10.1016/j.econlet.2022.110538
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    References listed on IDEAS

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    10. repec:hal:journl:peer-00732534 is not listed on IDEAS
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    More about this item

    Keywords

    Adaptive testing; Kernel method; Smoothing parameter selection;
    All these keywords.

    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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