## Stata 15 help for tobit_postestimation

```
[R] tobit postestimation -- Postestimation tools for tobit

Postestimation commands

The following postestimation commands are available after tobit:

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 nooffset]

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

statistic          Description
-------------------------------------------------------------------------
Main
xb               linear prediction; the default
stdp             standard error of the linear prediction
stdf             standard error of the forecast
pr(a,b)          Pr(a < y < b)
e(a,b)           E(y|a < y < b)
ystar(a,b)       E(y*),y* = max{a, min(y,b)}
-------------------------------------------------------------------------
These statistics are available both in and out of sample; type predict
... if e(sample) ... if wanted only for the estimation sample.
stdf is not allowed with svy estimation results.

where a and b may be numbers or variables; a missing (a > .) means minus
infinity, and b missing (b > .) means plus infinity; see missing.

Statistics > Postestimation

Description for predict

predict creates a new variable containing predictions such as linear
predictions, standard errors, probabilities, and expected values.

Options for predict

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

xb, the default, calculates the linear prediction.

stdp calculates the standard error of the prediction, which can be
thought of as the standard error of the predicted expected value or
mean for the observation's covariate pattern.  The standard error of
the prediction is also referred to as the standard error of the
fitted value.

stdf calculates the standard error of the forecast, which is the standard
error of the point prediction for 1 observation.  It is commonly
referred to as the standard error of the future or forecast value.
By construction, the standard errors produced by stdf are always
larger than those produced by stdp; see Methods and formulas in [R]
regress postestimation.

pr(a,b) calculates Pr(a < xb + u < 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 < xb + u < 30);
pr(lb,ub) calculates Pr(lb < xb + u < ub); and
pr(20,ub) calculates Pr(20 < xb + u < ub).

a missing (a > .) means minus infinity; pr(.,30) calculates
Pr(-infinity < xb + u < 30);
pr(lb,30) calculates Pr(-infinity < xb + u < 30) in observations for
which lb > .
and calculates Pr(lb < xb + u < 30) elsewhere.

b missing (b > .) means plus infinity; pr(20,.) calculates
Pr(+infinity > xb + u > 20);
pr(20,ub) calculates Pr(+infinity > xb + u > 20) in observations for
which ub > .
and calculates Pr(20 < xb + u < ub) elsewhere.

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

ystar(a,b) calculates E(y*), where y* = a if xb + u < a, y* = b if
xb + u > b, and y* = xb+u otherwise, meaning that y* is censored.  a
and b are specified as they are for pr().

nooffset is relevant only if you specified offset(varname).  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 first new variable will contain the derivative of the log
likelihood with respect to the regression equation.

The second new variable will contain the derivative of the log
likelihood with respect to the scale equation (sigma).

Syntax for margins

margins [marginlist] [, options]

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

statistic          Description
-------------------------------------------------------------------------
xb                 linear prediction; the default
pr(a,b)            Pr(a < y < b)
e(a,b)             E(y|a < y < b)
ystar(a,b)         E(y*),y* = max{a, min(y,b)}
stdp               not allowed with margins
stdf               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 linear predictions,
probabilities, and expected values.

Examples

Setup
. sysuse auto
. generate wgt = weight/100
. tobit mpg wgt, ll(17) ul(24)

Average marginal effects for all covariates
. margins, dydx(*)

Marginal effect on the truncated expected value, conditional on weights
of 2000 and 2500 pounds
. margins, dydx(wgt) predict(e(17,24)) at(wgt=(20 25))

```