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>.