Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/296329 
Year of Publication: 
2023
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 14 [Issue:] 2 [Year:] 2023 [Pages:] 511-569
Publisher: 
The Econometric Society, New Haven, CT
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.
Subjects: 
Bootstrap
cross-sectional dependence
spatial HAC
eigendecompo- sition
economic distance
JEL: 
C12
C32
C38
C52
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.