*! version 1.0.5 04jun2013
help spivregalso see: spivreg postestimation
spreg
spmat

Title

spivreg -- Spatial-autoregressive model with
spatial-autoregressive erros and additional endogenous variables

Syntax

spivreg depvar [varlist1] (varlist2
= [varlist_iv]) [if] [in], id(varname) [options]

Model
* id(varname) ID variable

dlmat(name) spmat object used in the
spatial-autoregressive term

elmat(name) spmat object used in the
spatial-error term

noconstant suppress constant term

heteroskedastic use the formula for the heteroskedastic error term

impower(q) use q powers of matrix W in forming the
instrument matrix H; default is 2

Maximization
maximize_options control the
maximization process; seldom used

* Required

Description

spivreg estimates the parameters of a cross-sectional
spatial-autoregressive model with spatial-autoregressive disturbances which
is known as a SARAR model when there are additional endogenous regressors.

A SARAR model includes a weighted average of the dependent variable, known
as a spatial lag, as a right-hand-side variable and it allows the
disturbance term to depend on a weighted average of the disturbances
corresponding to other units. The weights may differ for each observation
and are frequently inversely related to the distance from the current
observation. These weights must be stored in a spatial-weighting matrix
created by spmat.

spivreg estimates the parameters by generalized spatial two-stage least
squares (GS2SLS).

You can download Drukker, Prucha, and Raciborski (2011), which documents
this command, from
http://econweb.umd.edu/~prucha/Papers/WP_spivreg_2011.pdf.

Options

Model

id(varname) specifies a numeric variable that contains a unique
identifier for each observation. This option is required.

dlmat(name) specifies an spmat object
that contains the spatial-weighting matrix W to be used in the
spatial-autoregressive term.

elmat(name) specifies an spmat object
that contains the spatial-weighting matrix M to be used in the
spatial-error term.

noconstant suppresses the constant term in the model.

heteroskedastic specifies that spivreg use an estimator that
allows e to be heteroskedastically distributed over the observations.
By default, spivreg uses an estimator that assumes homoskedasticity.

impower(q) specifies how many powers of the matrix W
to include in calculating the instrument matrix H.
Integers in the set {2, 3, ..., floor(sqrt(cols(W)))}
are allowed, with q = 2 being the default.

Maximization

maximize_options:
iterate(#),
[no]log,
trace,
gradient,
showstep,
showtolerance,
tolerance(#), ltolerance(#),
from(init_specs); see [R] maximize. These options are seldom used.

Example

Setup

. use pollute

. spmat use cobj using pollute.spmat

Estimate the SARAR parameters

. spivreg pollution area (factories = penalties), id(id) dlmat(cobj) elmat(cobj)

Saved results

spivreg saves the following in e():

Scalars

e(N) number of observations

e(k) number of parameters

e(rho_2sls) initial estimate of rho

e(iterations) number of GMM iterations

e(iterations_2sls) number of 2SLS iterations

e(converged) 1 if GMM stage converged, 0 otherwise

e(converged_2sls) 1 if 2SLS stage converged, 0 otherwise

Macros

e(cmdline) command as typed

e(cmd) spivreg

e(model) sarar, sar, sare, or lr

e(het) heteroskedastic or homoskedastic

e(title) title in estimation output

e(depvar) name of dependent variable

e(indeps) names of independent variables

e(exogr) exogenous regressors

e(insts) instruments

e(instd) instrumented variables

e(constant) noconstant or hasconstant

e(H_omitted) names of omitted instruments in H

e(idvar) name of ID variable

e(dlmat) name of spmat object in dlmat()

e(elmat) name of spmat object in elmat()

e(predict) program used to implement predict

e(estat_cmd) program used to implement estat

e(properties) b V

Matrices

e(b) coefficient vector

e(V) variance-covariance matrix of the estimators

e(delta_2sls) initial estimate of lambda and b

Functions

e(sample) marks estimation sample

References

Drukker, D. M., I. R. Prucha, and R. Raciborski. 2011.
A command for estimating spatial-autoregressive models with spatial
autoregressive disturbances and additional endogenous variables.
Working paper, The University of Maryland, Department of Economics,
http://econweb.umd.edu/~prucha/Papers/WP_spivreg_2011.pdf.

Authors

David Drukker, StataCorp, College Station, TX.
ddrukker@stata.com.

Ingmar Prucha, Department of Economics, University of Maryland, College Park, MD.
prucha@econ.umd.edu.

Rafal Raciborski, StataCorp, College Station, TX.
rraciborski@stata.com.

Acknowledgment

We gratefully acknowledge financial support from the National Institute of
Health through the SBIR grant R43 AG027622 and R44 AG027622.

Also see

Online: spmat, spreg,
spmap, shp2dta, mif2dta (if installed)