Adaptive testing using data-driven method selecting smoothing parameters

被引：0
|
作者：
Wang, Luya [1 ,2 ]
机构：
[1] Capital Univ Econ & Business, Sch Stat, Beijing 100070, Peoples R China
[2] Capital Univ Econ & Business, Room 320,ChengMing Bldg, Beijing 100070, Peoples R China
来源：
ECONOMICS LETTERS | 2022年 / 215卷
基金：
中国国家社会科学基金;
关键词：
Adaptive testing; Kernel method; Smoothing parameter selection; MODEL-SPECIFICATION TESTS; NONPARAMETRIC-ESTIMATION; REGRESSION;
D O I：
10.1016/j.econlet.2022.110538
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
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. (C) 2022 Elsevier B.V. All rights reserved.
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页数：4
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