help spivreg postestimation                                also see:  spivreg  


spivreg postestimation -- Postestimation tools for spivreg


The following postestimation commands are available after spivreg:

command description ------------------------------------------------------------------------- INCLUDE help post_estat INCLUDE help post_estimates INCLUDE help post_lincom INCLUDE help post_lrtest INCLUDE help post_nlcom predict predicted values INCLUDE help post_predictnl INCLUDE help post_test INCLUDE help post_testnl -------------------------------------------------------------------------

Syntax for predict

predict [type] newvar [if] [in] [, statistic]

statistic Description ------------------------------------------------------------------------- Main naive predictions based on the observed values of y; the default xb linear prediction -------------------------------------------------------------------------

Options for predict

+------+ ----+ Main +-------------------------------------------------------------

naive predicted values based on the observed values of y, Y*g + lambda*W*y + X*b.

xb calculates the linear prediction X*b.


The methods implemented in predict after spivreg are documented in Drukker, Prucha, and Raciborski (2011) which can be downloaded from

The predictor computed by the option naive will generally be biased; see Kelejian and Prucha (2007) for an explanation.

See Remarks in spreg postestimation for a more detailed discussion of biased and unbiased spatial predictors.


Setup . use pollute . spmat use cobj using pollute.spmat . spivreg pollution area (factories = penalties), id(id) dlmat(cobj) elmat(cobj)

Obtain predicted values based on the observed values of y . predict yhat


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,

Kelejian H. H., and I. R. Prucha. 2007. The relative efficiencies of various predictors in spatial econometric models containing spatial lags. Regional Science and Urban Economics 37, 363-374.

Also see

Online: spivreg, spreg (if installed)