The CSPATIALREG Procedure
PROC CSPATIALREG Features
The CSPATIALREG procedure fits spatial data regression models by maximum likelihood techniques.
PROC CSPATIALREG supports the following models:
linear model
linear model with spatial lag of X (SLX) effects
spatial autoregressive (SAR) model
spatial Durbin model (SDM)
spatial error model (SEM)
spatial Durbin error model (SDEM)
spatial moving average (SMA) model
spatial Durbin moving average (SDMA) model
spatial autoregressive moving average (SARMA) model
spatial Durbin autoregressive moving average (SDARMA) model
spatial autoregressive confused (SAC) model
spatial Durbin autoregressive confused (SDAC) model
The following list summarizes some basic features of the CSPATIALREG procedure:
uses model-building syntax that includes CLASS and effect-based MODEL statements that are familiar from other SAS Econometrics analytic procedures
enables you to fit multiple models by using maximum likelihood estimation
provides a TEST statement for performing linear tests of hypotheses
provides a RESTRICT statement for imposing linear parameter constraints
provides an OUTPUT statement for producing a data table that contains predictions and residuals
Because the CSPATIALREG procedure runs on SAS Cloud Analytic Services (CAS), it also does the following:
enables you to run on a cluster of machines that distribute the data and the computations
enables you to run in single-machine mode
exploits all the available cores and concurrent threads. For information about how PROC CSPATIALREG uses threads, see the section Multithreading in Chapter 3: Shared Concepts.