Spatial Seemingly Unrelated Regression Models
Description
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) .