Wspc

A spatial weight matrix row-standardized based on first order
contiguity criterium.

datasets

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) <doi:10.18637/jss.v104.i11>
Mur, J., Lopez, F.A., 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>.

Roman Minguez

spsur

Spatial Seemingly Unrelated Regression Models

Ana Angulo

Fernando A Lopez

Jesus Mur

Wspc function

A row-standardized squared matrix with 25 rows and columns.
The rows and columns follow the same order than provinces included in
spc data frame.

Format

Spatial weight matrix for South-West Ohio Counties to estimate
Spatial Phillips-Curve — Wspc

Spatial weight matrix for South-West Ohio Counties to estimate
Spatial Phillips-Curve

Wspc: Spatial weight matrix for South-West Ohio Counties to estimate Spatial Phillips-Curve

Description

Usage

Arguments

Format

References