| Autor: |
Timothy G. Conley, Sílvia Gonçalves, Min Seong Kim, Benoit Perron |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Quantitative Economics. 14:511-569 |
| ISSN: |
1759-7323 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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