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: Mur, J., Lopez, F., 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> and Lopez, F.A., Minguez, R. and Mur, J. (2020) <doi:10.1007/s00168-019-00914-1>.

Depends: R (≥ 4.1), methods (≥ 4.1), stats (≥ 4.1)
Imports: car (≥ 3.0-12), Formula (≥ 1.2-4), ggplot2 (≥ 3.3.5), gmodels (≥ 2.18.1), gridExtra (≥ 2.3), knitr (≥ 1.37), lmtest (≥ 0.9-40), MASS (≥ 7.3-56), Matrix (≥ 1.4-0), minqa (≥ 1.2.4), numDeriv (≥ 2016.8-1.1), rlang (≥ 1.0.1), Rdpack (≥ 2.1.4), rmarkdown (≥ 2.12), sparseMVN (≥ 0.2.2), spatialreg (≥ 1.2-3), spdep (≥ 1.2-2), sphet (≥ 2.0)
Suggests: bookdown (≥ 0.24), dplyr (≥ 1.0.8), sf (≥ 1.0-7)
Published: 2022-04-22
Author: Ana Angulo [aut], Fernando A Lopez [aut], Roman Minguez [aut, cre], Jesus Mur [aut]
Maintainer: Roman Minguez <roman.minguez at uclm.es>
BugReports: https://github.com/rominsal/spsur/issues
License: GPL-3
URL: https://CRAN.R-project.org/package=spsur
NeedsCompilation: no
Citation: spsur citation info
In views: Econometrics, Spatial
CRAN checks: spsur results


Reference manual: spsur.pdf
Vignettes: spsur user guide
Maximum Likelihood estimation of Spatial Seemingly Unrelated Regression models. A short Monte Carlo exercise with spsur and spse
spsur vs spatialreg
Spatial seemingly unrelated regression models. A comparison of spsur, spse and PySAL


Package source: spsur_1.0.2.1.tar.gz
Windows binaries: r-devel: spsur_1.0.2.1.zip, r-release: spsur_1.0.2.1.zip, r-oldrel: spsur_1.0.2.1.zip
macOS binaries: r-release (arm64): spsur_1.0.2.1.tgz, r-oldrel (arm64): spsur_1.0.2.1.tgz, r-release (x86_64): spsur_1.0.2.1.tgz, r-oldrel (x86_64): spsur_1.0.2.1.tgz
Old sources: spsur archive


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