doi:10.18637/jss.v107.i08>. It supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3> and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) <doi:10.1198/073500107000000188>. It also allows for either Probit or Logit model and compute the average marginal effects. All these models are presented in Sarrias and Piras (2023) <doi:10.1016/j.jocm.2023.100432>.">

spldv: Spatial Models for Limited Dependent Variables (original) (raw)

The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators <doi:10.18637/jss.v107.i08>. It supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3> and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) <doi:10.1198/073500107000000188>. It also allows for either Probit or Logit model and compute the average marginal effects. All these models are presented in Sarrias and Piras (2023) <doi:10.1016/j.jocm.2023.100432>.

Version: 0.1.3
Depends: R (≥ 4.0)
Imports: Formula, Matrix, maxLik, stats, sphet, memisc, car, methods, numDeriv, MASS, spatialreg
Suggests: spdep
Published: 2023-10-11
DOI: 10.32614/CRAN.package.spldv
Author: Mauricio Sarrias ORCID iD [aut, cre], Gianfranco Piras ORCID iD [aut], Daniel McMillen [ctb]
Maintainer: Mauricio Sarrias
BugReports: https://github.com/gpiras/spldv/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/gpiras/spldv
NeedsCompilation: no
Citation: spldv citation info
Materials: NEWS
CRAN checks: spldv results

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