spsur
Spatial Seemingly Unrelated Regression Models
A collection of functions to test and estimate Seemingly Unrelated Regression (usually called SUR) models, with spatial structure, by maximum likelihood and three-stage least squares. The package estimates the most common spatial specifications, that is, SUR with Spatial Lag of X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), SUR with Spatial Durbin Error Model (called SUR-SDEM), SUR with Spatial Autoregressive terms and Spatial Autoregressive Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM). The methodology of these models can be found in next references Minguez, R., Lopez, F.A., and Mur, J. (2022)
- Version1.0.2.5
- R version≥ 4.1
- LicenseGPL-3
- Needs compilation?No
- spsur citation info
- Last release10/29/2022
Documentation
- Vignettespsur user guide
- VignetteMaximum Likelihood estimation of Spatial Seemingly Unrelated Regression models. A short Monte Carlo exercise with spsur and spse
- Vignettespsur vs spatialreg
- VignetteSpatial seemingly unrelated regression models. A comparison of spsur, spse and PySAL
- In ViewsEconometrics
- In ViewsSpatial
Team
Roman Minguez
Ana Angulo
Show author detailsRolesAuthorFernando A Lopez
Show author detailsRolesAuthorJesus Mur
Show author detailsRolesAuthor
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- Depends2 packages
- Imports14 packages
- Suggests3 packages