GMM Estimators for Binary Spatial Models in R

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Gianfranco Piras, Mauricio Sarrias
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引用次数: 0

Abstract

Despite the huge availability of software to estimate cross-sectional spatial models, there are only few functions to estimate models dealing with spatial limited dependent variable. This paper fills this gap introducing the new R package spldv. The package is based on generalized methods of moment (GMM) estimators and includes a series of one- and two-step estimators based on different choices of the weighting matrix for the moments conditions in the first step, and different estimators for the variance-covariance matrix of the estimated coefficients. An important feature of spldv is that users can estimate the spatial Durbin model and compute the direct, indirect, and total effects in a friendly and flexible way.
二元空间模型的GMM估计
尽管有大量可用的软件来估计横截面空间模型,但只有很少的函数来估计处理空间有限因变量的模型。本文介绍了新的R包spldv,填补了这一空白。该软件包基于广义矩估计方法,包括一系列基于第一步矩条件加权矩阵选择的一步和两步估计器,以及估计系数的方差-协方差矩阵的不同估计器。spldv的一个重要特点是用户可以友好灵活地估计空间Durbin模型,计算直接、间接和总效应。
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 Software
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
40
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.
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