GMM Estimators for Binary Spatial Models in R
Dublin Core
Title
GMM Estimators for Binary Spatial Models in R
Subject
binary dependent variables, spatial model, GMM, R.
Description
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
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
Creator
Gianfranco Piras
Source
https://www.jstatsoft.org/article/view/v107i08
Publisher
The Catholic University of America
University of Chieti-Pescara
University of Chieti-Pescara
Date
September 2023
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Gianfranco Piras, “GMM Estimators for Binary Spatial Models in R,” Repository Horizon University Indonesia, accessed April 19, 2025, https://repository.horizon.ac.id/items/show/8311.