Higher-order spatial autoregressive varying coefficient model: estimation and specification test

Tizheng Li () and Yuping Wang
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Tizheng Li: Xi’an University of Architecture and Technology
Yuping Wang: Xi’an University of Architecture and Technology

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2024, vol. 33, issue 4, No 11, 1258-1299

Abstract: Abstract Conventional higher-order spatial autoregressive models assume that regression coefficients are constant over space, which is overly restrictive and unrealistic in applications. In this paper, we introduce higher-order spatial autoregressive varying coefficient model where regression coefficients are allowed to smoothly change over space, which enables us to simultaneously explore different types of spatial dependence and spatial heterogeneity of regression relationship. We propose a semi-parametric generalized method of moments estimation method for the proposed model and derive asymptotic properties of resulting estimators. Moreover, we propose a testing method to detect spatial heterogeneity of the regression relationship. Simulation studies show that the proposed estimation and testing methods perform quite well in finite samples. The Boston house price data are finally analyzed to demonstrate the proposed model and its estimation and testing methods.

Keywords: Spatial dependence; Spatial heterogeneity; Higher-order spatial autoregressive varying coefficient model; Semi-parametric generalized method of moments; Generalized likelihood ratio statistic; Bootstrap; 91B72; 62G05; 62G10; 62G20 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11749-024-00944-8

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