**[R] ivprobit postestimation** -- Postestimation tools for ivprobit

__Postestimation commands__

The following postestimation commands are of special interest after
**ivprobit**:

Command Description
-------------------------------------------------------------------------
**estat classification** report various summary statistics, including the
classification table
**estat correlation** report the correlation matrix of the errors of
the dependent variable and the endogenous
variables
**estat covariance** report the covariance matrix of the errors of the
dependent variable and the endogenous variables
**lroc** compute area under ROC curve and graph the curve
**lsens** graph sensitivity and specificity versus
probability cutoff
-------------------------------------------------------------------------
These commands are not appropriate after the two-step estimator or the
**svy** prefix.

The following standard postestimation commands are also available:

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; not available with
two-step estimator
**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
-------------------------------------------------------------------------
* **estat ic**, **forecast**, and **suest** are not appropriate after **ivprobit,**
**twostep**.
+ **forecast**, **hausman**, and **lrtest** are not appropriate with **svy** estimation
results.

__Syntax for predict__

After ML or twostep

**predict** [*type*] *newvar* [*if*] [*in*] [**,** *statistic* __rule__**s** **asif**]

After ML

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

*statistic* Description
-------------------------------------------------------------------------
Main
**xb** linear prediction; the default
**stdp** standard error of the linear prediction
__p__**r** probability of a positive outcome accounting for
endogeneity; not available with two-step
estimator
-------------------------------------------------------------------------
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 probabilities.

__Options for predict__

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

**xb**, the default, calculates the linear prediction.

**stdp** calculates the standard error of the linear prediction.

**pr** calculates the probability of a positive outcome accounting for
endogeneity. **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 p endogenous regressors, p + {(p + 1)(p + 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 (p + 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[p+1,1], s[2,2], ..., s[p+1,2], ..., s[p+1,p+1], where
s[m,n] denotes the (m,n) element of the Cholesky decomposition of
the error covariance matrix.

__Syntax for margins__

**margins** [*marginlist*] [**,** *options*]

**margins** [*marginlist*] **,** __pr__**edict(***statistic *...**)** [__pr__**edict(***statistic *...**)**
...] [*options*]

*statistic* Description
-------------------------------------------------------------------------
**xb** linear prediction; the default
__p__**r** probability of a positive outcome accounting for
endogeneity; not available with two-step estimator
**stdp** 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 and
probabilities.

__Syntax for estat__

Correlation matrix

**estat** __cor__**relation** [**,** __bor__**der(***bspec***)** **left(***#***)** __for__**mat(***%fmt***)**]

Covariance matrix

**estat** __cov__**ariance** [**,** __bor__**der(***bspec***)** **left(***#***)** __for__**mat(***%fmt***)**]

__Menu for estat__

**Statistics > Postestimation**

__Description for estat__

**estat correlation** displays the correlation matrix of the errors of the
dependent variable and the endogenous variables.

**estat covariance** displays the covariance matrix of the errors of the
dependent variable and the endogenous variables.

**estat correlation** and **estat covariance** are not allowed after the **ivprobit**
two-step estimator.

__Options for estat__

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

**border(***bspec***)** sets border style of the matrix display. The default is
**border(all)**.

**left(***#***)** sets the left indent of the matrix display. The default is
**left(2)**.

**format(***%fmt***)** specifies the format for displaying the individual elements
of the matrix. The default is **format(%9.0g)**.

__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(pr)**

Same as above, but specify no children
**. margins, dydx(fem_educ) predict(pr) at(kids=0)**