BOOTSTRAP LONG MEMORY PROCESSES IN THE FREQUENCY DOMAIN

• 成果类型: Article
• 署名作者: Hidalgo, Javier
• 署名单位: University of London; London School Economics & Political Science
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/20-AOS2006
• 发表日期: 2021
• 页码: 1407-1435
• 关键词: gaussian semiparametric estimation; time-series regression; range; inference

摘要

The aim of the paper is to describe a bootstrap, contrary to the sieve boot-strap, valid under either long memory (LM) or short memory (SM) dependence. One of the reasons of the failure of the sieve bootstrap in our context is that under LM dependence, the sieve bootstrap may not be able to capture the true covariance structure of the original data. We also describe and examine the validity of the bootstrap scheme for the least squares estimator of the parameter in a regression model and for model specification. The motivation for the latter example comes from the observation that the asymptotic distribution of the test is intractable.