Bootstrap inference under cross‐sectional dependence

IF 1.9 3区 经济学 Q2 ECONOMICS
Timothy G. Conley, Sílvia Gonçalves, Min Seong Kim, B. Perron
{"title":"Bootstrap inference under cross‐sectional dependence","authors":"Timothy G. Conley, Sílvia Gonçalves, Min Seong Kim, B. Perron","doi":"10.3982/qe1626","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross‐ sectional/spatial dependence, which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent bootstrap of Shao (2010) and generates data by multiplying a vector of independently and identically distributed external variables by the eigendecomposition of a bootstrap kernel. We prove the validity of our method for studentized and unstudentized statistics under a linear array representation of the data. Simulation experiments document the potential for improved inference with our approach. We illustrate our method in a firm‐level regression application investigating the relationship between firms' sales growth and the import activity in their local markets using unique firm‐level and imports data for Canada.","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":"1 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Economics","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.3982/qe1626","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 2

Abstract

In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross‐ sectional/spatial dependence, which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent bootstrap of Shao (2010) and generates data by multiplying a vector of independently and identically distributed external variables by the eigendecomposition of a bootstrap kernel. We prove the validity of our method for studentized and unstudentized statistics under a linear array representation of the data. Simulation experiments document the potential for improved inference with our approach. We illustrate our method in a firm‐level regression application investigating the relationship between firms' sales growth and the import activity in their local markets using unique firm‐level and imports data for Canada.
横截面依赖下的自举推理
在本文中,我们介绍了一种生成具有未知横截面/空间依赖模式的bootstrap样本的方法,我们称之为空间依赖野生bootstrap。该方法与Shao(2010)的野生依赖自举(wild dependent bootstrap)的空间对应,通过将独立且分布相同的外部变量向量乘以自举核的特征分解来生成数据。我们在数据的线性数组表示下证明了我们的方法对学生化和非学生化统计的有效性。模拟实验证明了用我们的方法改进推理的潜力。我们在公司层面的回归应用中说明了我们的方法,该应用使用加拿大独特的公司层面和进口数据来调查公司销售增长与当地市场进口活动之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.10
自引率
5.60%
发文量
28
审稿时长
52 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信