Specifying spatial effects in panel data: Locally robust vs. conditional tests

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics Pub Date : 2025-09-23 DOI:10.1016/j.spasta.2025.100934

Giovanni Millo

引用次数: 0

Abstract

We address the issue of specifying a spatial lag vs. spatial error process in spatial panel models. The popular locally robust Lagrange multiplier (RLM) tests for spatial lag vs. error are compared to optimal alternatives based on maximum likelihood estimation: Wald and likelihood ratio (LR) tests requiring estimation of the full encompassing model, and conditional Lagrange multiplier (CLM) tests drawing on the reduced specification. Monte Carlo simulations are performed in a typical spatial panel context. Individual effects are successfully eliminated through the forward orthogonal deviations transformation, making the RLM suitable for panel data. Nevertheless, the statistical properties of Wald and LR are superior to those of the RLM. The CLM also dominates the RLM, as long as the sample is at least of moderate size. The RLM are computationally very convenient, but ML-based tests are feasible in most usage cases on mainstream hardware.

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指定面板数据中的空间效果：局部鲁棒测试与条件测试

我们解决了在空间面板模型中指定空间滞后与空间误差过程的问题。将流行的局部鲁棒拉格朗日乘数（RLM）空间滞后与误差测试与基于最大似然估计的最佳替代方案进行比较：Wald和似然比（LR）测试需要估计完整的包含模型，以及基于简化规范的条件拉格朗日乘数（CLM）测试。蒙特卡罗模拟是在典型的空间面板环境中进行的。通过前向正交偏差变换，成功地消除了个体影响，使RLM适用于面板数据。然而，Wald和LR的统计性质优于RLM。只要样本至少是中等大小，CLM也支配着RLM。RLM在计算上非常方便，但是基于ml的测试在主流硬件上的大多数使用情况下是可行的。

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.