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) doi:10.18637/jss.v104.i11 Mur, J., Lopez, F.A., and Herrera, M. (2010) doi:10.1080/17421772.2010.516443 Lopez, F.A., Mur, J., and Angulo, A. (2014) doi:10.1007/s00168-014-0624-2.
- 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
- Materialspsur.pdf
- Materialspsur user guide
- MaterialMaximum Likelihood estimation of Spatial Seemingly Unrelated Regression models. A short Monte Carlo exercise with spsur and spse
- Materialspsur vs spatialreg
- MaterialSpatial 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
Insights
Last 30 days
This package has been downloaded 381 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 26 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 5,079 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Jul 23, 2024 with 94 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports13 packages
- Suggests3 packages