Adaptive testing using data-driven method selecting smoothing parameters

Luya Wang

Economics Letters, 2022, vol. 215, issue C

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.

Keywords: Adaptive testing; Kernel method; Smoothing parameter selection (search for similar items in EconPapers)
JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:215:y:2022:i:c:s0165176522001495

DOI: 10.1016/j.econlet.2022.110538

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