Bootstrap inference under cross-sectional dependence

• 成果类型: Article
• 署名作者: Conley, Timothy G.; Goncalves, Silvia; Kim, Min Seong; Perron, Benoit
• 署名单位: Western University (University of Western Ontario); McGill University; Universite de Montreal; Universite de Montreal; University of Connecticut; Universite de Montreal
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1626
• 发表日期: 2023
• 页码: 511-569
• 关键词: Bootstrap; cross-sectional dependence; spatial HAC; eigendecomposition; economic distance; C12; C32; C38; C52

摘要

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.