Stata 15 help for oprobit_postestimation

[R] oprobit postestimation -- Postestimation tools for oprobit

Postestimation commands

The following postestimation commands are available after oprobit:

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 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 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. forecast is also not appropriate with mi estimation results.

Syntax for predict

predict [type] {stub* | newvar | newvarlist} [if] [in] [, statistic outcome(outcome) nooffset]

predict [type] {stub* | newvarlist} [if] [in] , scores

statistic Description ------------------------------------------------------------------------- Main pr predicted probabilities; the default xb linear prediction stdp standard error of the linear prediction ------------------------------------------------------------------------- If you do not specify outcome(), pr (with one new variable specified) assumes outcome(#1). You specify one or k new variables with pr, where k is the number of outcomes. You specify one new variable with xb and stdp. 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 probabilities, linear predictions, and standard errors.

Options for predict

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

pr, the default, calculates the predicted probabilities. If you do not also specify the outcome() option, you specify k new variables, where k is the number of categories of the dependent variable. Say that you fit a model by typing oprobit result x1 x2, and result takes on three values. Then you could type predict p1 p2 p3 to obtain all three predicted probabilities. If you specify the outcome() option, you must specify one new variable. Say that result takes on the values 1, 2, and 3. Typing predict p1, outcome(1) would produce the same p1.

xb calculates the linear prediction. You specify one new variable, for example, predict linear, xb. The linear prediction is defined, ignoring the contribution of the estimated cutpoints.

stdp calculates the standard error of the linear prediction. You specify one new variable, for example, predict se, stdp.

outcome(outcome) specifies for which outcome the predicted probabilities are to be calculated. outcome() should contain either one value of the dependent variable or one of #1, #2, ..., with #1 meaning the first category of the dependent variable, #2 meaning the second category, etc.

nooffset is relevant only if you specified offset(varname) for oprobit. It modifies the calculations made by predict so that they ignore the offset variable; the linear prediction is treated as xb rather than as xb + offset.

scores calculates equation-level score variables. The number of score variables created will equal the number of outcomes in the model. If the number of outcomes in the model was k, then

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

The other new variables will contain the derivative of the log likelihood with respect to the cutpoints.

Syntax for margins

margins [marginlist] [, options]

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

statistic Description ------------------------------------------------------------------------- default probabilities for each outcome pr probability for a specified outcome xb linear prediction stdp not allowed with margins ------------------------------------------------------------------------- pr defaults to the first outcome.

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 probabilities and linear predictions.


Setup . webuse fullauto . oprobit rep77 i.foreign length mpg

Predicted probabilities of an excellent repair record . predict exc if e(sample), outcome(5)

Histogram of predicted probabilities . histogram exc

Linear prediction . predict pscore, xb

Average marginal effects on the probability of the worst repair record . margins, dydx(*) predict(outcome(1))

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