A nonparametric test for the heterogeneity of the spatial autoregressive parameter

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference Pub Date : 2025-05-10 DOI:10.1016/j.jspi.2025.106298

Yangbing Tang , Jiang Du , Zhongzhan Zhang

引用次数: 0

Abstract

We propose a new test for the heterogeneity of the spatial autoregressive parameter in semiparametric varying-coefficient spatial autoregressive models. Our specification test is built on the difference of parametric and nonparametric estimates of the spatial autoregressive coefficient, where the two estimates are obtained by the sieve GMM estimation method. Under mild conditions, we derive the limiting null distribution, the local power property and consistency of the test statistic. Numerical simulations show promising performance of the proposed test for finite samples in the considered cases, and the crime data of Tokyo is analyzed to illustrate the usefulness of the test.

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空间自回归参数异质性的非参数检验

本文提出了一种检验半参数变系数空间自回归模型中空间自回归参数异质性的新方法。我们的规范检验建立在空间自回归系数的参数和非参数估计的差异上，其中两个估计是通过筛选GMM估计方法获得的。在温和条件下，导出了检验统计量的极限零分布、局部功率性质和一致性。数值模拟结果表明，该方法在有限样本情况下具有良好的性能，并对东京的犯罪数据进行了分析，以说明该方法的有效性。

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

Journal of Statistical Planning and Inference 数学-统计学与概率论

CiteScore

2.10

自引率

11.10%

发文量

审稿时长

3-6 weeks

期刊介绍： The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists. We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.