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), 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
Minguez, R., Lopez, F.A., and Mur, J. (2022)
Mur, J., Lopez, F.A., and Herrera, M. (2010)
Lopez, F.A., Mur, J., and Angulo, A. (2014) .