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线性回归完，可以求Y的预测值
这里一样啊；模型8估计完，可以求出预测值h*
Title
[R] oprobit postestimation -- Postestimation tools for oprobit
Description
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
(1) forecast dynamic forecasts and simulations
lincom point estimates, standard errors, testing, and inference for linear combinations of
coefficients
linktest link test for model specification
(2) 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
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(1) forecast is not appropriate with mi or svy estimation results.
(2) lrtest is not appropriate with svy 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
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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 > Predictions, residuals, etc.
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.
Examples
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
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