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

__Postestimation commands__

The following standard postestimation commands are available after
**churdle**:

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.

__Syntax for predict__

**predict** [*type*] *newvar* [*if*] [*in*] [**,** *statistic* __e__**quation(***eqno***)**]

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

*statistic* Description
-------------------------------------------------------------------------
Main
__ys__**tar** conditional expectation of *depvar*; the default
__r__**esiduals** residuals
__ys__**tar(***a***,***b***)** E(y*), y*=max{*a*, min(y,*b*)}
**xb** linear prediction
**stdp** standard error of the linear prediction
__p__**r(***a***,***b***)** Pr(*a* < y < *b*)
**e(***a***,***b***)** E(y | *a* < y < *b*)
-------------------------------------------------------------------------
These statistics are available both in and out of sample; type **predict**
*...* **if e(sample)** *...* if wanted only for the estimation sample.

where *a* and *b* may be numbers or variables; *a* missing (*a* __>__ **.**) means minus
infinity, and *b* missing (*b* __>__ **.**) means plus infinity; see missing.
For **churdle exponential**, *b* is **.** (missing).

__Menu for predict__

**Statistics > Postestimation**

__Description for predict__

**predict** creates a new variable containing predictions such as conditional
expectation of *depvar*, residuals, linear predictions, standard errors,
and probabilities.

__Options for predict__

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

**ystar**, the default, calculates the conditional expectation of the
dependent variable.

**residuals** calculates the residuals.

**ystar(***a***,***b***)** calculates E(y*). *a* and *b* are specified as they are for **pr()**.
If *a* and *b* are equal to the lower and upper bounds specified in
**churdle**, then E(y*) is equivalent to the predicted value of the
dependent variable **ystar**.

**xb** calculates the linear prediction.

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

**pr(***a***,***b***)** calculates Pr(*a* < y < *b*), the probability that y|x would be
observed in the interval (*a*,*b*).

*a* and *b* may be specified as numbers or variable names; *lb* and *ub* are
variable names;
**pr(20,30)** calculates Pr(20 < y<30);
**pr(***lb***,***ub***)** calculates Pr(*lb* < y < *ub*); and
**pr(20,** *ub***)** calculates Pr(20 < y < *ub*).

*a* missing (*a* __>__ **.**) means **ll**; **pr(.,30)** calculates Pr(*ll* < y < 30);
**pr(***lb***,30)** calculates Pr(*ll* < y < 30) in observations for which *lb* __>__ **.**
and calculates Pr(*lb* < y < 30) elsewhere.

*b* missing (*b* __>__ **.**) means plus infinity; **pr(20,.)** calculates
Pr(infinity > y > 20);
**pr(20,***ub***)** calculates Pr(infinity > y > 20) in observations for which
*ub* __>__ **.**
and calculates Pr(*ub* > y > 20) elsewhere.
For **churdle linear**, *ul* is infinity.

**e(***a***,***b***)** calculates E(y | *a* < y < *b*), the expected value of y|x conditional
on y|x being in the interval (*a*,*b*), meaning that y|x is bounded. *a*
and *b* are specified as they are for **pr()**.

**equation(***eqno***)** specifies the equation for which predictions are to be
made for the **xb** and **stdp** options. **equation()** should contain either
one equation name or one of **#1**, **#2**, ... with **#1** meaning the first
equation, **#2** meaning the second equation, etc.

If you do not specify **equation()**, the results are the same as if you
specified **equation(# 1)**.

**scores** calculates the equation-level score variables. If you specify one
new variable, the scores for the latent-variable equation are
computed. If you specify a variable list, the scores for each
equation are calculated. The following scores may be obtained:

the first new variable will contain partial ln L/partial(x beta),

the second new variable will contain partial ln L/partial(z
gamma_{ll}),

the third new variable will contain partial ln L/partial(z
gamma_{ul}),

the fourth new variable will contain partial ln L/partial(sigma),

the fifth new variable will contain partial ln L/partial(sigma_{ll}),
and

the sixth new variable will contain partial ln L/partial(sigma_{ul}).

__Syntax for margins__

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

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

*statistic* Description
-------------------------------------------------------------------------
Main
__ys__**tar** conditional expectation of *depvar*; the default
__ys__**tar(***a***,***b***)** E(y*), y*=max{*a*, min(y,*b*)}; for **churdle exponential** *b*
is .
**xb** linear prediction
__p__**r(***a***,***b***)** Pr(*a* < y < *b*); for **churdle exponential** *b* is .
**e(***a***,***b***)** E(y | *a* < y < *b*); for **churdle exponential** *b* is .
__r__**esiduals** not allowed with **margins**
**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 conditional expectations,
linear predictions, and probabilities.

__Examples__

Setup
**. webuse fitness**
**. churdle linear hours age i.smoke distance i.single, select(commute**
**whours) ll(0) het(age single) nolog**

Fit conditional expectation the dependent variable **hours**
**. predict hourshat**

Fit conditional expectation for values of the dependent variable greater
than zero
**. predict exercises, e(0,.)**
**. summarize hours hourshat exercises**

Average marginal effect of **single**
**. margins, dydx(1.single)**