Work

Fast Cluster Bootstrap Methods for Spatial Error Models

Year: 2025

Type: article

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.

Source: Mathematics

DOI: 10.3390/math13182913

Authors: Yu Zheng, Honggang Fan

Institutions: China University of Geosciences (Beijing), Renmin University of China

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities