*! version 1.0.2 11feb2011

*! version 1.0.3 21jan2012

*! version 1.0.4 25jan2016

*! version 1.0.6 27jun2016

help xsmle postestimation also see: xsmle

Title

xsmle postestimation -- Postestimation tools for xsmle

Description

The following postestimation commands are available after xsmle:

command description

estat AIC, BIC, VCE, and estimation sample summary

predict predicted values

Syntax for predict

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

statistic

Main
rform reduced-form predicted values; the default

full predictions based on a full information set

limited predictions based on a limited information set

naive predictions based on the observed values of y

xb linear prediction

a the fixed or random-effects

noie exclude fixed or random-effects from the prediction

Options for predict

Main

rform predicted values calculated from the reduced-form equation,
y_it= (I-rho*W)^(-1)*(x_it*beta + a_i).

full predicted values based on the full information set.
This option is available only for a SAC model.

limited predicted values based on the limited information set.
This option is available only for a SAC model.

naive predicted values based on the observed values of y_it,
rho*W*y_it + x_it*beta + a_i.

xb calculates the linear prediction x_it*beta + a_i.

a estimates a_i's, the fixed or random-effects. In the case of fixed-effects models, this statistic is allowed only when type(ind).

noie the estimated a_i's are not included in the prediction.

Remarks

The methods implemented in predict after xsmle are the panel data extensions
of those available in Drukker, Prucha, and Raciborski (2013) for the cross-sectional case.
See Kelejian and Prucha (2007) for more details.

Examples

rform after SAR model with random-effects

SAR model

use http://www.econometrics.it/stata/data/xsmle/product.dta, clear

spmat use usaww using http://www.econometrics.it/stata/data/xsmle/usaww.spmat

gen lngsp = log(gsp)

gen lnpcap = log(pcap)

gen lnpc = log(pc)

gen lnemp = log(emp)

xsmle lngsp lnpcap lnpc lnemp unemp, wmat(usaww)

predict y_hat

full information predictor after SAC model with fixed-effects

xsmle lngsp lnpcap lnpc lnemp, fe model(sac) wmat(usaww) emat(usaww)

predict y_hat, full

References

Drukker, D. M., I. R. Prucha, and R. Raciborski. 2013.
Maximum likelihood and generalized spatial two-stage least-squares estimators
for a spatial-autoregressive model with spatial-autoregressive disturbances.
Stata Journal 13(2): 221–241.

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(3): 363–374.

Authors

Federico Belotti

Centre for Economic and International Studies, University of Rome Tor Vergata

Rome, Italy

federico.belotti@uniroma2.it

Gordon Hughes

University of Edinburgh

Edinburgh, UK

g.a.hughes@ed.ac.uk

Andrea Piano Mortari

Centre for Economic and International Studies, University of Rome Tor Vergata

Rome, Italy

andrea.piano.mortari@uniroma2.it

Also see

Online: xsmle, spreg (if installed), spivreg (if installed)