Stata 15 help for eintreg_postestimation

[ERM] eintreg postestimation -- Postestimation tools for eintreg

Postestimation commands

The following postestimation command is of special interest after eintreg:

Command Description ------------------------------------------------------------------------- estat teffects treatment effects and potential-outcome means -------------------------------------------------------------------------

The following postestimation commands are available after eintreg:

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) estat (svy) postestimation statistics for survey data estimates cataloging estimation results * forecast dynamic forecasts and simulations * hausman Hausman's specification test lincom point estimates, standard errors, testing, and inference for linear combinations of coefficients * 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 suest seemingly unrelated estimation test Wald tests of simple and composite linear hypotheses testnl Wald tests of nonlinear hypotheses ------------------------------------------------------------------------- * forecast, hausman, and lrtest are not appropriate with svy estimation results.


Predictions after eintreg are described in

[ERM] eintreg predict predict after eintreg [ERM] predict treatment predict for treatment statistics [ERM] predict advanced predict’s advanced features

[ERM] eintreg predict describes the most commonly used predictions. If you fit a model with treatment effects, predictions specifically related to these models are detailed in [ERM] predict treatment. [ERM] predict advanced describes less commonly used predictions, such as predictions of outcomes in auxiliary equations.

Syntax for margins

margins [marginlist] [, options]

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

statistic Description ------------------------------------------------------------------------- Main mean mean; the default pr probability of binary or ordinal y pomean potential-outcome mean te treatment effect tet treatment effect on the treated xb linear prediction pr(a,b) Pr(a < y < b) for continuous y e(a,b) E(y | a < y < b) for continuous y ystar(a,b) E(y*), y* = max{a,min(y,b)} for continuous y -------------------------------------------------------------------------

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 means, probabilities, potential-outcome means, treatment effects, and linear predictions.


Setup . webuse class10 . eintreg gpal gpau income, endogenous(hsgpa = income i.hscomp) . generate str name = "Billy" in 537

Expected values for college GPA if we fix Billy's hsgpa at 2.00 and at 3.00 and only consider his value of income . margins if name=="Billy", at(hsgpa=(2 3)) predict(fix(hsgpa))

Expected values for college GPA if we fix Billy's hsgpa at 2.00 and at 3.00 and if we also account for his values of hsgpa, hscomp, and unobservable factors that lead to correlation between hsgpa and college GPA . generate hsgpaT = hsgpa . margins if name=="Billy", at(hsgpa=(2 3)) predict(base(hsgpa=hsgpaT))

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