# Spatial Modeling

### Important note

In what follows, I describe some modules for spatial data that were available before Stata introduced their own `sp`

commands for the analysis of spatial data in version 15.

As far as I can judge, the modules I describe here are still available and working. For information about the inbuilt `sp`

commands, see Stata's help system (`help sp`

will provide access to the introductory remarks and an overview of all available commands).

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