GMM Estimators for Binary Spatial Models in R

被引:0
|
作者
Piras, Gianfranco [1 ,2 ]
Sarrias, Mauricio [3 ]
机构
[1] Catholic Univ Amer, Sch Arts & Sci, Dept Econ, Washington, DC 20064 USA
[2] Univ G dAnnunzio, Dept Econ Studies, Pescara, Italy
[3] Univ Talca, Fac Econ & Negocios FEN, Sn Ave Lircay, Talca, Chile
来源
JOURNAL OF STATISTICAL SOFTWARE | 2023年 / 107卷 / 08期
关键词
binary dependent variables; spatial model; GMM; R; AUTOREGRESSIVE MODEL; DISTURBANCES; LIKELIHOOD;
D O I
10.18637/jss.v107.i08
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
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页码:1 / 33
页数:33
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