# Spatial Modeling

This entry is mainly about two classes of models: those provided by Maurizio Pisati's spatreg and those by spreg by David Drukker and collaborators. I will start, however, with the spatial diagnostics provided by Pisati.

## Spatial diagnostics

Spatial diagnostics were developed mainly to test whether data analyzed via OLS regression exhibit spatial correlation. In other words, it is assumed that you just have estimated a linear regression model. Now,

spatdiag, weights(my-wmatrix)

will compute statistics that investigate spatial correlation among the residuals and help you judge whether a spatial lag or a spatial error model might be more appropriate.

## Spatreg

Procedure spatreg requires a spatial weight matrix plus an eigenvalue vector pertaining to this matrix; both can be computed (if your data permit) by Pisani's spatwmat (see entry on spatial data).

spatreg can estimate spatial lag and spatial error models. The basic commands are:

spatreg depvar indepvars, weights(w-matrix) eigenval(e-vector) model(lag)

and

spatreg depvar indepvars, weights(w-matrix) eigenval(e-vector) model(error)

## Spreg

Procedure spreg estimates a spatial-autoregressive model with spatial-autoregressive disturbances, offering two estimation methods. It requires two matrices, created by spmat, one for the spatial-autoregressive term and one for the spatial-error term. Both matrices can be (and often will be) identical, but have to be indicated both nevertheless.

The basis setup for model estimation is

spreg ml depvar indepvars, id(id-var) dlmat(w-matrix) elmat(w-matrix)

for maximum likelihood estimation, or

spreg gs2sls depvar indepvars, id(id-var) dlmat(w-matrix) elmat(w-matrix)

for generalized spatial two-stage least squares estimation.

id-var is an ID variable that was created by spmat, and the matrices provided with dlmat and elmat indicate the weights for the spatial-autoregressive and the error term, respectively.

© W. Ludwig-Mayerhofer, Stata Guide | Last update: 17 Aug 2016