包括非参数回归测试

IF 1 4区经济学 Q3 ECONOMICS

Econometric Theory Pub Date : 2024-04-17 DOI:10.1017/s0266466624000100

Elia Lapenta, Pascal Lavergne

{"title":"包括非参数回归测试","authors":"Elia Lapenta, Pascal Lavergne","doi":"10.1017/s0266466624000100","DOIUrl":null,"url":null,"abstract":"We set up a formal framework to characterize encompassing of nonparametric models through the $L^2$ distance. We contrast it to previous literature on the comparison of nonparametric regression models. We then develop testing procedures for the encompassing hypothesis that are fully nonparametric. Our test statistics depend on kernel regression, raising the issue of bandwidth’s choice. We investigate two alternative approaches to obtain a “small bias property” for our test statistics. We show the validity of a wild bootstrap method. We empirically study the use of a data-driven bandwidth and illustrate the attractive features of our tests for small and moderate samples.","PeriodicalId":49275,"journal":{"name":"Econometric Theory","volume":"100 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ENCOMPASSING TESTS FOR NONPARAMETRIC REGRESSIONS\",\"authors\":\"Elia Lapenta, Pascal Lavergne\",\"doi\":\"10.1017/s0266466624000100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We set up a formal framework to characterize encompassing of nonparametric models through the $L^2$ distance. We contrast it to previous literature on the comparison of nonparametric regression models. We then develop testing procedures for the encompassing hypothesis that are fully nonparametric. Our test statistics depend on kernel regression, raising the issue of bandwidth’s choice. We investigate two alternative approaches to obtain a “small bias property” for our test statistics. We show the validity of a wild bootstrap method. We empirically study the use of a data-driven bandwidth and illustrate the attractive features of our tests for small and moderate samples.\",\"PeriodicalId\":49275,\"journal\":{\"name\":\"Econometric Theory\",\"volume\":\"100 1\",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2024-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Econometric Theory\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1017/s0266466624000100\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometric Theory","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1017/s0266466624000100","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}

引用次数: 0

摘要

我们建立了一个正式框架，通过 $L^2$ 距离来描述非参数模型的包含性。我们将其与之前关于非参数回归模型比较的文献进行对比。然后，我们开发了完全非参数的包含假设检验程序。我们的检验统计依赖于核回归，这就提出了带宽选择的问题。我们研究了两种替代方法，以获得检验统计量的 "小偏差属性"。我们证明了野生引导法的有效性。我们对数据驱动带宽的使用进行了实证研究，并说明了我们的检验对小样本和中等样本的吸引力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

ENCOMPASSING TESTS FOR NONPARAMETRIC REGRESSIONS

We set up a formal framework to characterize encompassing of nonparametric models through the

$L^2$

distance. We contrast it to previous literature on the comparison of nonparametric regression models. We then develop testing procedures for the encompassing hypothesis that are fully nonparametric. Our test statistics depend on kernel regression, raising the issue of bandwidth’s choice. We investigate two alternative approaches to obtain a “small bias property” for our test statistics. We show the validity of a wild bootstrap method. We empirically study the use of a data-driven bandwidth and illustrate the attractive features of our tests for small and moderate samples.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Econometric Theory MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY

CiteScore

1.90

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Since its inception, Econometric Theory has aimed to endow econometrics with an innovative journal dedicated to advance theoretical research in econometrics. It provides a centralized professional outlet for original theoretical contributions in all of the major areas of econometrics, and all fields of research in econometric theory fall within the scope of ET. In addition, ET fosters the multidisciplinary features of econometrics that extend beyond economics. Particularly welcome are articles that promote original econometric research in relation to mathematical finance, stochastic processes, statistics, and probability theory, as well as computationally intensive areas of economics such as modern industrial organization and dynamic macroeconomics.