Bootstrap inference under cross-sectional dependence

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...

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Bibliographic Details
Published in:Quantitative economics 2023-05, Vol.14 (2), p.511-569
Main Authors: Conley, Timothy G, Gonçalves, Sílvia, Kim, Min Seong, Perron, Benoit
Format: Article
Language:English
Subjects:
Bootstrap
C12
C32
C38
C52
cross-sectional dependence
Econometrics
economic distance
eigendecompo- sition
eigendecomposition
Euclidean space
Fourier transforms
Hypotheses
Hypothesis testing
Imports
Random variables
Regression analysis
Sales
Simulation
Spacetime
spatial HAC
Time series
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Summary: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.
ISSN:1759-7331
1759-7323
1759-7331
DOI:10.3982/QE1626