Stata 15 help for frontier_postestimation

[R] frontier postestimation -- Postestimation tools for frontier

Postestimation commands

The following postestimation commands are available after frontier:

Command Description ------------------------------------------------------------------------- contrast contrasts and ANOVA-style joint tests of estimates estat ic Akaike's and Schwarz's Bayesian information criteria (AIC and BIC) estat summarize summary statistics for the estimation sample estat vce variance-covariance matrix of the estimators (VCE) estimates cataloging estimation results hausman Hausman's specification test lincom point estimates, standard errors, testing, and inference for linear combinations of coefficients linktest link test for model specification lrtest likelihood-ratio test margins marginal means, predictive margins, marginal effects, and average marginal effects marginsplot graph the results from margins (profile plots, interaction plots, etc.) nlcom point estimates, standard errors, testing, and inference for nonlinear combinations of coefficients predict predictions, residuals, influence statistics, and other diagnostic measures predictnl point estimates, standard errors, testing, and inference for generalized predictions pwcompare pairwise comparisons of estimates test Wald tests of simple and composite linear hypotheses testnl Wald tests of nonlinear hypotheses -------------------------------------------------------------------------

Syntax for predict

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

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

statistic Description ------------------------------------------------------------------------- Main xb linear prediction; the default stdp standard error of the prediction u estimates of minus the natural log of the technical efficiency via E(u|e) m estimates of minus the natural log of the technical efficiency via M(u|e) te estimates of the technical efficiency via E{exp(-su)|e} s = 1, for production functions s = -1, for cost functions ------------------------------------------------------------------------- These statistics are available both in and out of sample; type predict...if e(sample)... if wanted only for the estimation sample.

Menu for predict

Statistics > Postestimation

Description for predict

predict creates a new variable containing predictions such as linear predictions, standard errors, and estimates of technical efficiency.

Options for predict

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

xb, the default, calculates the linear prediction.

stdp calculates the standard error of the linear prediction.

u produces estimates of minus the natural log of the technical efficiency via E(u|e).

m produces estimates of minus the natural log of the technical efficiency via M(u|e).

te produces estimates of the technical efficiency via E{exp(-su)|e}.

scores calculates equation-level score variables.

The first new variable will contain the derivative of the log likelihood with respect to the regression equation.

The second new variable will contain the derivative of the log likelihood with respect to the second equation (lnsig2v).

The third new variable will contain the derivative of the log likelihood with respect to the third equation (lnsig2u).

Syntax for margins

margins [marginlist] [, options]

margins [marginlist] , predict(statistic ...) [options]

statistic Description ------------------------------------------------------------------------- xb linear prediction; the default stdp not allowed with margins u not allowed with margins m not allowed with margins te not allowed with margins -------------------------------------------------------------------------

Statistics not allowed with margins are functions of stochastic quantities other than e(b).

For the full syntax, see [R] margins.

Menu for margins

Statistics > Postestimation

Description for margins

margins estimates margins of response for linear predictions.

Examples

Setup . webuse greene9

Fit stochastic frontier model and use capital to model the idiosyncratic error variance . frontier lnv lnk lnl, vhet(capital)

Estimate technical efficiency . predict efficiency, te

Use mean of conditional error distribution to estimate minus log efficiency . predict mlogeffmean, u

Use mode of conditional error distribution to estimate minus log efficiency . predict mlogeffmode, m


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