Stata 15 help for churdle_postestimation

```
[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
* 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 equation(eqno)]

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

statistic            Description
-------------------------------------------------------------------------
Main
ystar              conditional expectation of depvar; the default
residuals          residuals
ystar(a,b)         E(y*), y*=max{a, min(y,b)}
xb                 linear prediction
stdp               standard error of the linear prediction
pr(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).

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

statistic          Description
-------------------------------------------------------------------------
Main
ystar            conditional expectation of depvar; the default
ystar(a,b)       E(y*), y*=max{a, min(y,b)}; for churdle exponential b
is .
xb               linear prediction
pr(a,b)          Pr(a < y < b); for churdle exponential b is .
e(a,b)           E(y | a < y < b); for churdle exponential b is .
residuals        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.

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)

```