A data set from Anselin (1988, p. 203-2011) used to estimate a Spatial
Phillips-Curve for 25 counties in South-West Ohio for two time
periods (1981 and 1983).

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

spc function

A data frame with 25 rows and 10 variables:<dl>
 <dt>COUNTY</dt>
<dd>County coded as a name.</dd> <dt>WAGE83</dt>
<dd>Changes in wage rates for 1983.</dd> <dt>UN83</dt>
<dd>Inverse unemployment rate in 1983.</dd> <dt>NMR83</dt>
<dd>Net migration rate 1983.</dd> <dt>SMSA</dt>
<dd>Dummy variable to identify counties defined as
 Standard Metropolitan Statistical Areas (SMSA = 1).</dd> <dt>WAGE82</dt>
<dd>Changes in wage rates for 1982.</dd> <dt>WAGE81</dt>
<dd>Changes in wage rates for 1981.</dd> <dt>UN80</dt>
<dd>Inverse unemployment rate in 1980.</dd> <dt>NMR80</dt>
<dd>Net migration rate 1983.</dd> <dt>WAGE80</dt>
<dd>changes in wage rates.</dd> <dt>geometry</dt>
<dd>geometry of sf object.</dd>
</dl>

Format

A classical Spatial Phillips-Curve — spc

A data frame with 25 rows and 10 variables:<dl>
 <dt>COUNTY</dt>
<dd>County coded as a name.</dd>

 <dt>WAGE83</dt>
<dd>Changes in wage rates for 1983.</dd>

 <dt>UN83</dt>
<dd>Inverse unemployment rate in 1983.</dd>

 <dt>NMR83</dt>
<dd>Net migration rate 1983.</dd>

 <dt>SMSA</dt>
<dd>Dummy variable to identify counties defined as
 Standard Metropolitan Statistical Areas (SMSA = 1).</dd>

 <dt>WAGE82</dt>
<dd>Changes in wage rates for 1982.</dd>

 <dt>WAGE81</dt>
<dd>Changes in wage rates for 1981.</dd>

 <dt>UN80</dt>
<dd>Inverse unemployment rate in 1980.</dd>

 <dt>NMR80</dt>
<dd>Net migration rate 1983.</dd>

 <dt>WAGE80</dt>
<dd>changes in wage rates.</dd>

 <dt>geometry</dt>
<dd>geometry of sf object.</dd>


</dl>

spc: A classical Spatial Phillips-Curve

Description

Usage

Arguments

Format

References