**[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
**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
**linktest** link test for model specification
* **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* __nooff__**set**]

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

*statistic* Description
-------------------------------------------------------------------------
Main
**xb** linear prediction; the default
**stdp** standard error of the linear prediction
**stdf** standard error of the forecast
__p__**r(***a***,***b***)** Pr(a < y < b)
**e(***a***,***b***)** *E*(y|a < y < b)
__ys__**tar(***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.

__Menu for predict__

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

*statistic* Description
-------------------------------------------------------------------------
**xb** linear prediction; the default
__p__**r(***a***,***b***)** Pr(a < y < b)
**e(***a***,***b***)** *E*(y|a < y < b)
__ys__**tar(***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**.

__Menu for 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))**