A modified bootstrap for kernel-based specification test with heavy-tailed data

被引：0

作者：

Huang, Ta-Cheng ^{[1
]}

Li, Hongjun ^{[2
]}

Li, Zheng ^{[3
]}

机构：

[1] Natl Univ Singapore, Global Asia Inst, Singapore, Singapore

[2] Capital Univ Econ & Business, Int Sch Econ & Management, Beijing, Peoples R China

[3] North Carolina State Univ, Dept Agr & Resource Econ, Raleigh, NC 27695 USA

来源：

ECONOMICS LETTERS | 2020年 / 189卷

基金：

美国国家科学基金会;

关键词：

Wild bootstrap; Kernel-based test; Specification test; CENTRAL-LIMIT-THEOREM; NONPARAMETRIC-ESTIMATION; CONSISTENT;

D O I：

10.1016/j.econlet.2020.108986

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This paper provides a new resampling strategy to improve the finite sample performance of a nonparametric kernel-based specification test in the presence of heavy-tailed error terms. Based on the test statistic of Li and Wang (1998), we propose to generate the bootstrapped samples using a modified wild bootstrap. This new method matches all moments of the error terms if the error has a symmetric distribution and matches the first and all even moments when error distribution is asymmetric around zero. This new resampling method has better finite sample performance than the traditional one when the distribution of the error terms is symmetric and heavy-tailed. (c) 2020 Elsevier B.V. All rights reserved.

引用

页数：4

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