Fast Cluster Bootstrap Methods for Spatial Error Models

Yu Zheng and Honggang Fan ()
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Yu Zheng: School of Science, China University of Geosciences (Beijing), Beijing 100083, China
Honggang Fan: School of Mathematics, Renmin University of China, Beijing 100872, China

Mathematics, 2025, vol. 13, issue 18, 1-16

Abstract: Typically, the traditional bootstrap methods for parameter inference of spatial error models suffer from high computational costs, so this study proposes fast cluster bootstrap methods for spatial error models to deal with the dilemma. The key idea is to calculate the sufficient statistics for each cluster before performing the bootstrap loop of the spatial error model, and based on these sufficient statistics, all quantities needed for bootstrap inference can be computed. Furthermore, this study performed Monte Carlo simulations, and the result reveals that compared with traditional bootstrap methods, our proposed methods can reduce the computational cost substantially and improve the reliability for obtaining the bootstrap test statistics and confidence intervals of the parameters for spatial error models.

Keywords: spatial error model; pairs cluster bootstrap; wild cluster bootstrap; computational cost (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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