A nonparametric spatial regression model using partitioning estimators

IF 2.5 Q2 ECONOMICS Econometrics and Statistics Pub Date : 2023-02-20 DOI:10.1016/j.ecosta.2023.02.003
Jose Olmo , Marcos Sanso-Navarro
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引用次数: 0

Abstract

Conventional spatial regression models are extended by modelling the spatial effects of the exogenous regressor model (SLX) as a functional coefficient. This coefficient is estimated by partitioning the domain of the spatial variable into a set of disjoint intervals and approximating the function using local Taylor expansions. The asymptotic properties of the proposed partitioning estimator are derived, and pointwise and uniform tests for the presence of spatial effects are developed. An empirical application of this work is used to study environmental Engel curves and provides strong evidence of neighbouring effects in the relationship between households’ income and the amount of pollution embodied in the goods and services they consume.
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基于分区估计的非参数空间回归模型
通过将外生回归模型(SLX)的空间效应建模为功能系数,扩展了传统的空间回归模型。该系数是通过将空间变量的域划分为一组不相交的区间并使用局部泰勒展开式逼近函数来估计的。导出了所提出的分区估计量的渐近性质,并给出了空间效应存在的点向检验和一致检验。这项工作的经验应用被用于研究环境恩格尔曲线,并提供了强有力的证据,证明家庭收入与他们消费的商品和服务中体现的污染量之间的关系存在邻近效应。
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
84
期刊介绍: Econometrics and Statistics is the official journal of the networks Computational and Financial Econometrics and Computational and Methodological Statistics. It publishes research papers in all aspects of econometrics and statistics and comprises of the two sections Part A: Econometrics and Part B: Statistics.
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