Bootstrap Inference Under Cross Sectional Dependence

Timothy Conley, Silvia Goncalves (), Min Seong Kim and Benoit Perron ()

No 2022-14, Working papers from University of Connecticut, Department of Economics

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.

Keywords: bootstrap; cross sectional dependence; spatial HAC; eigendecomposition; economic distance (search for similar items in EconPapers)
JEL-codes: C12 C32 C38 C52 (search for similar items in EconPapers)
Pages: 54 pages
Date: 2022-07
New Economics Papers: this item is included in nep-ecm
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https://media.economics.uconn.edu/working/2022-14.pdf Full text (application/pdf)

Related works:
Journal Article: Bootstrap inference under cross‐sectional dependence (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:uct:uconnp:2022-14

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