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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) 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) .

Maintainer: Roman Minguez
Author(s): Ana Angulo [aut], Fernando A Lopez [aut], Roman Minguez [aut, cre], Jesus Mur [aut]

License: GPL-3

Uses: Formula, gmodels, MASS, Matrix, minqa, numDeriv, sparseMVN, spatialreg, spdep, knitr, Rdpack, dplyr, rmarkdown, bookdown, sf

Released about 1 month ago.

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