Package: spsur 1.0.2.1

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>.

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