spsur:一个处理空间看似不相关回归模型的R包

IF 5.4 2区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Statistical Software Pub Date : 2022-01-01 DOI:10.18637/jss.v104.i11

R. Mínguez, F. López, J. Mur

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引用次数: 4

摘要

空间看似不相关回归(Spatial SUR)模型是一种有用的多方程计量经济学规范，可以同时包含空间效应和跨方程的相关误差项。spsur R包的目的是提供一套完整的函数来测试SUR模型残差中的空间结构;通过应用不同的方法来估计最流行的规范，并测试参数的线性限制。该方案还有助于估计所谓的空间影响，方便地适应SUR框架。该软件包包括模拟具有用户决定的特征的数据集的功能，这可能在教学活动或更一般的研究项目中有用。文章最后用一个真实的数据应用程序显示了spsur在第一次封锁期间必须研究地理区域内个人流动性与西班牙COVID-19发病率之间关系的潜力。©2022，美国统计协会。版权所有。

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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spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models

Spatial seemingly unrelated regression (spatial SUR) models are a useful multiequational econometric specification to simultaneously incorporate spatial effects and correlated error terms across equations. The purpose of the spsur R package is to supply a complete set of functions to test for spatial structures in the residual of a SUR model;to estimate the most popular specifications by applying different methods and test for linear restrictions on the parameters. The package also facilitates the estimation of so-called spatial impacts, conveniently adapted to a SUR framework. The package includes functions to simulate datasets with the features decided by the user, which may be useful in teaching activities or in more general research projects. The article concludes with a real data application showing the potential that spsur has to examine the relation of individual mobility over geographic areas and the incidence of COVID-19 in Spain during the first lockdown. © 2022, American Statistical Association. All rights reserved.

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来源期刊

Journal of Statistical Software 工程技术-计算机：跨学科应用

CiteScore

10.70

自引率

1.70%

发文量

审稿时长

6-12 weeks

期刊介绍： The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.