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 
[aut, cre],
Gianfranco Piras
[aut],
Daniel McMillen [ctb] |
| Maintainer: |
Mauricio Sarrias <msarrias86 at gmail.com> |
| 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 |