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) Lopez, F.A., Mur, J., and Angulo, A. (2014) .


Reference manual

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install.packages("spsur") by Roman Minguez, a month ago

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Authors: Ana Angulo [aut] , Fernando A Lopez [aut] , Roman Minguez [aut, cre] , Jesus Mur [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports car, Formula, ggplot2, gmodels, gridExtra, knitr, lmtest, MASS, Matrix, minqa, numDeriv, rlang, Rdpack, rmarkdown, sparseMVN, spatialreg, spdep

Depends on methods, stats

Suggests bookdown, dplyr, sf

See at CRAN