A Projection Framework for Testing Shape Restrictions That Form Convex Cones

• 成果类型: Article
• 署名作者: Fang, Zheng; Seo, Juwon
• 署名单位: Texas A&M University System; Texas A&M University College Station; National University of Singapore
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA17764
• 发表日期: 2021
• 页码: 2439-2458
• 关键词: nonparametric approach; inference; MONOTONICITY; regression; series; rates

摘要

This paper develops a uniformly valid and asymptotically nonconservative test based on projection for a class of shape restrictions. The key insight we exploit is that these restrictions form convex cones, a simple and yet elegant structure that has been barely harnessed in the literature. Based on a monotonicity property afforded by such a geometric structure, we construct a bootstrap procedure that, unlike many studies in nonstandard settings, dispenses with estimation of local parameter spaces, and the critical values are obtained in a way as simple as computing the test statistic. Moreover, by appealing to strong approximations, our framework accommodates nonparametric regression models as well as distributional/density-related and structural settings. Since the test entails a tuning parameter (due to the nonstandard nature of the problem), we propose a data-driven choice and prove its validity. Monte Carlo simulations confirm that our test works well.