help ivprobit postestimation dialogs: predict estat
lroc lsens
also see: ivprobit
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Title
[R] ivprobit postestimation -- Postestimation tools for ivprobit
Description
The following postestimation commands are of special interest after
ivprobit:
command description
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estat classification reports various summary statistics, including the
classification table
lroc graphs the ROC curve and calculates the area under
the curve
lsens graphs sensitivity and specificity versus
probability cutoff
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These commands are not appropriate after the two-step estimator or the
svy prefix.
For information about these commands, see [R] logistic postestimation.
The following standard postestimation commands are also available:
command description
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(1) estat AIC, BIC, VCE, and estimation sample summary
estat (svy) postestimation statistics for survey data
estimates cataloging estimation results
hausman Hausman's specification test
lincom point estimates, standard errors, testing, and inference
for linear combinations of coefficients
(2) lrtest likelihood-ratio test; not available with two-step
estimator
margins marginal means, predictive margins, marginal effects,
and average marginal effects
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
(1) suest seemingly unrelated estimation
test Wald tests of simple and composite linear hypotheses
testnl Wald tests of nonlinear hypotheses
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(1) estat ic and suest are not appropriate after ivprobit, twostep.
(2) lrtest is not appropriate with svy estimation results.
Syntax for predict
After ML or twostep
predict [type] newvar [if] [in] [, statistic rules asif]
After ML
predict [type] {stub*|newvarlist} [if] [in] , scores
statistic description
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Main
xb linear prediction; the default
stdp standard error of the linear prediction
pr probability of a positive outcome; not available with
two-step estimator
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These statistics are available both in and out of sample; type predict
... if e(sample) ... if wanted only for the estimation sample.
Menu
Statistics > Postestimation > Predictions, residuals, etc.
Options for predict
+------+
----+ Main +-------------------------------------------------------------
xb, the default, calculates the linear prediction.
stdp calculates the standard error of linear prediction.
pr calculates the probability of a positive outcome. pr is not available
with the two-step estimator.
rules requests that Stata use any rules that were used to identify the
model when making the prediction. By default, Stata calculates
missing for excluded observations. rules is not available with the
two-step estimator.
asif requests that Stata ignore the rules and the exclusion criteria and
calculate predictions for all observations possible using the
estimated parameters from the model. asif is not available with the
two-step estimator.
scores, not available with twostep, calculates equation-level score
variables.
For models with one endogenous regressor, four new variables are
created.
The first new variable will contain the first derivative of the
log likelihood with respect to the probit equation.
The second new variable will contain the first derivative of the
log likelihood with respect to the reduced-form equation for the
endogenous regressor.
The third new variable will contain the first derivative of the
log likelihood with respect to atanh(rho).
The fourth new variable will contain the first derivative of the
log likelihood with respect to ln(sigma).
For models with j endogenous regressors, j + {(j + 1)(j + 2)}/2 new
variables are created.
The first new variable will contain the first derivative of the
log likelihood with respect to the probit equation.
The second through (j + 1)th new variables will contain the first
derivatives of the log likelihood with respect to the
reduced-form equations for the endogenous variables in the order
they were specified when ivprobit was called.
The remaining score variables will contain the partial
derivatives of the log likelihood with respect to s[2,1], s[3,1],
..., s[j+1,1], s[2,2], ..., s[j+1,2], ..., s[j+1,j+1], where
s[m,n] denotes the (m,n) element of the Cholesky decomposition of
the error covariance matrix.
Examples
Setup
. webuse laborsup
. ivprobit fem_work fem_educ kids (other_inc = male_educ)
Compute average marginal effect of fem_educ on probability that a woman
works
. margins, dydx(fem_educ) predict(p)
Also see
Manual: [R] ivprobit postestimation
Help: [R] ivprobit
