*! version 1.1.3 23jan2012
help sfpanel postestimationalso see: sfpanel

Title

sfpanel postestimation -- Postestimation tools for sfpanel

Description

The following postestimation commands are available for sfpanel.

command description

estat AIC, BIC, VCE, and estimation sample summary

predict predictions, residuals, influence statistics, and other diagnostic measures

Syntax for predict

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

predict[type]{stub*|newvar_xb newvar_v newvar_u}[if] [in], scores

statistic

Main
xb linear prediction; the default

stdp standard error of the prediction

u estimates of (technical or cost) inefficiency via E(u|e) (Jondrow et al., 1982)

u0 estimates of (technical or cost) inefficiency via E(u|e) when the random effect is zero; only after model(tre)

m estimates of (technical or cost) inefficiency via M(u|e)

jlms estimates of (technical or cost) efficiency via exp[-E(u|e)]

bc estimates of (technical or cost) efficiency via E[exp(-u)|e] (Battese and Coelli, 1988)

ci estimates of confidence interval for (technical or cost) inefficiency/efficiency

marginal marginal effects of the exogenous determinants on the unconditional mean and variance of the inefficiency (Wang, 2002)

trunc(tlevel) truncation of estimated efficiency/inefficiency

scores calculates score variables

These statistics are only available for the estimation sample.

Options for predict

Main

xb, the default, calculates the linear prediction.

stdp calculates the standard error of the linear prediction.

u produces estimates of (technical or cost) inefficiency via E(u|e) using the Jondrow et al. (1982) estimator.

u0 produces estimates of the (technical or cost) inefficiency via
E(u|e) assuming the random effect is zero. This statistic is allowed only when the estimation is performed with the model(tre) option.

m produces estimates of (technical or cost) inefficiency via M(u|e), the mode of the conditional distribution u|e. This statistic is not allowed when the estimation is performed with model(fels),
model(fecss), model(fe) or model(regls) option.

jlms produces estimates of (technical or cost) efficiency via exp[-E(u|e)].

bc produces estimates of (technical or cost) efficiency via E[exp(-u)|e] using the Battese and Coelli (1988) estimator. This statistic is allowed when the estimation is performed with model(tfe), model(tre), model(bc95),
model(bc92), model(kumb90), model(bc88) and model(pl81).

ci computes confidence interval using the approach proposed by Horrace and Schmidt (1996).
This option can be used together with u or jlms or bc. It is not allowed when the estimation is performed with model(fels), model(fecss), model(fe) or model(regls)
or when the estimation is performed with model(bc92) or model(kumb90) and bc has been specified.
The default confidence level is 95, meaning a 95% confidence interval.
If the option level(#) is used in the previous estimation command, the confidence interval will be computed using the # level.
This option creates two additional variables: newvar_LBcilevel
and newvar_UBcilevel, that are the lower and the upper bound, respectively.

marginal calculates the marginal effects of the exogenous determinants on E(u) and Var(u) using the approach proposed by Wang (2002).
The marginal effects are observation-specific and are saved in the new variables varname_m_M and varname_u_V, the marginal effects on the unconditional mean and variance of inefficiency, respectively.
varname_m and varname_u are the names of each exogenous determinants specified in options emean(varlist_m [,noconstant])
and usigma(varlist_u [,noconstant]). marginal can be used only if the estimation
is performed with model(bc95) or when the inefficiency in model(tfe) or model(tre) is distribution(tnormal).
When they are both specified, varlist_m and varlist_u must contain the same variables in the same order.
This option can be specified in two ways: i) together with either u, m, jlms or bc; ii) alone without specifying newvar.

trunc(tlevel) excludes from the inefficiency estimation the units whose effects are, at least at one time period, in the upper and bottom tlevel% range. trunc() can be used only if the estimation is performed
with model(fe), model(regls), model(fecss) and model(fels).

scores calculates score variables. This option is not allowed when the estimation is performed with the option model(fecss), model(fels), model(fe) or model(regls).
When the argument of the option model() is tfe or bc95 scores are defined as the derivative of the objective function with respect to the parameters.
When the argument of the option model() is tre, bc88, bc92, kumb90 or pl81 scores are defined as the derivative of the objective function with respect to the coefficients.
This difference is due to the different moptimize() evaluator type used to implement the estimators (See mata moptimize()).

Remarks

When the sfpanel command is used to estimate production frontiers, predict will provide the post-estimation of technical (in)efficiency,
while when the sfpanel command is used to estimate cost frontiers, predict will provide the post-estimation of cost (in)efficiency.
It is worth noting that sfpanel and the related predict command follow the definitions of technical and cost (in)efficiency given in Kumbhakar and Lovell (2000).

Examples

True fixed-effects model (Greene, 2005)

. webuse xtfrontier1, clear

. sfpanel lnwidgets lnworkers lnmachines, m(tfe) usigma(lnworkers) robust

Linear prediction

. predict xb

Technical inefficiency

. predict ineffmean, u

. predict ineffmode, m

Technical efficiency

. predict jlms, jlms

Technical efficiency/inefficiency confidence intervals

. predict ineffmean, u ci

. predict bc, bc ci

. predict jlms, jlms ci

Non-monotonic marginal effects

. webuse xtfrontier1, clear

. sfpanel lnwidgets lnworkers lnmachines, m(tfe) d(tnormal) e(lnworkers) u(lnworkers) robust

. predict, marg

Score variables

. predict score*, scores

Authors

Federico Belotti

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

Rome, Italy

federico.belotti@uniroma2.it

Silvio Daidone

Centre for Health Economics, University of York

York, UK

silvio.daidone@york.ac.uk

Vincenzo Atella

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

Rome, Italy

atella@uniroma2.it

Giuseppe Ilardi

Economic and Financial Statistics Department, Bank of Italy

Rome, Italy

giuseppe.ilardi@bancaditalia.it

Also see

Help: sfpanel, sfcross, sfcross_postestimation.