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Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 14 [Issue:] 2 [Year:] 2023 [Pages:] 511-569
Verlag: 
The Econometric Society, New Haven, CT
Zusammenfassung: 
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.
Schlagwörter: 
Bootstrap
cross-sectional dependence
spatial HAC
eigendecompo- sition
economic distance
JEL: 
C12
C32
C38
C52
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