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 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 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.

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] , 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.

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))


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