A J-test for spatial autoregressive binary models

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics Pub Date : 2025-04-28 DOI:10.1016/j.spasta.2025.100903

Gianfranco Piras , Mauricio Sarrias

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引用次数: 0

Abstract

Spatial autoregressive binary models are well established in spatial statistics and econometric literature. Recently, different estimation methods have been proposed that account for logistic as well as probit regressions. In spatial models the choice of the spatial weighting matrix is crucial to reflect the amount of correlation in the data. This article proposes a simple

J

-test procedure for spatial autoregressive binary model. Since the

J

-test is a non-nested test, it can be used, among other things, to test the specification of the spatial weighting matrix. The

J

-test is based on augmenting the null model with the predictor from the alternative model(s). After defining these predictors, we develop the theory and derive the steps for the

J

-test. We also evaluate the finite sample properties in the context of a Monte Carlo experiment. An empirical application on firms’ decisions to reopen in the aftermath of Hurricane Katrina for New Orleans is also presented.

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空间自回归二元模型的j检验

空间自回归二元模型在空间统计学和计量经济学文献中得到了很好的建立。最近，人们提出了不同的估计方法，既考虑了逻辑回归，也考虑了概率回归。在空间模型中，空间加权矩阵的选择是反映数据中相关程度的关键。本文提出了空间自回归二元模型的一个简单的j检验程序。由于J-test是一个非嵌套测试，因此它可以用于测试空间加权矩阵的规格。j检验是基于用来自备选模型的预测器对零模型进行扩充。在定义了这些预测因子之后，我们发展了理论并推导了j检验的步骤。我们还在蒙特卡罗实验的背景下评估了有限样本的性质。本文还对新奥尔良市卡特里娜飓风过后企业重新开业的决策进行了实证分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Spatial Statistics GEOSCIENCES, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

4.00

自引率

21.70%

发文量

审稿时长

55 days

期刊介绍： Spatial Statistics publishes articles on the theory and application of spatial and spatio-temporal statistics. It favours manuscripts that present theory generated by new applications, or in which new theory is applied to an important practical case. A purely theoretical study will only rarely be accepted. Pure case studies without methodological development are not acceptable for publication. Spatial statistics concerns the quantitative analysis of spatial and spatio-temporal data, including their statistical dependencies, accuracy and uncertainties. Methodology for spatial statistics is typically found in probability theory, stochastic modelling and mathematical statistics as well as in information science. Spatial statistics is used in mapping, assessing spatial data quality, sampling design optimisation, modelling of dependence structures, and drawing of valid inference from a limited set of spatio-temporal data.