Bootstrap inference under cross‐sectional dependence

Timothy Conley, Sílvia Gonçalves, Min Seong Kim and Benoit Perron

Quantitative Economics, 2023, vol. 14, issue 2, 511-569

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.

Date: 2023
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https://doi.org/10.3982/QE1626

Related works:
Working Paper: Bootstrap Inference Under Cross Sectional Dependence (2022)
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