*! version 1.1.0: 18apr2022

help rbiprobit postestimationgithub.com/cobanomics/rbiprobit

also see: help rbiprobit

Postestimation commands

The following postestimation commands are of special interest after rbiprobit:

Command Description

rbiprobit margdec marginal means, predictive margins,
marginal effects, and average marginal effects of indepvars and indepvars_en

rbiprobit tmeffects treatment effects of treatment
variable depvar_en

The following postestimation commands are available after rbiprobit:

Command Description

predict predictions, residuals, influence statistics, and other diagnostic measures

* hausman and lrtest are not appropriate with svy estimation results.

Syntax for predict

predict
[type]
newvar
[if] [in]
[, statistic nooffset]

predict
[type]
{stub*|newvar_eq1 newvar_eq2
newvar_atanrho}
[if] [in]
,
scores

statistic

Main
p11 Pr(depvar=1, depvar_en=1); the default

p10 Pr(depvar=1, depvar_en=0)

p01 Pr(depvar=0, depvar_en=1)

p00 Pr(depvar=0, depvar_en=0)

pmarg1 Pr(depvar=1); marginal success probability for outcome equation

pmarg2 Pr(depvar_en=1); marginal success probability for treatment equation

pcond1 Pr(depvar=1 | depvar_en=1)

pcond2 Pr(depvar_en=1 | depvar=1)

xb1 linear prediction for outcome equation

xb2 linear prediction for treatment equation

stdp1 standard error of the linear prediction for outcome equation

stdp2 standard error of the linear prediction for treatment equation

Description for predict

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

Options for predict

p11, the default, calculates the joint predicted probability
Pr(depvar=1, depvar_en=1).

p10 calculates the joint predicted probability
Pr(depvar=1,depvar_en=0).

p01 calculates the joint predicted probability
Pr(depvar=0,depvar_en=1).

p00 calculates the joint predicted probability
Pr(depvar=0,depvar_en=0).

pmarg1 calculates the univariate (marginal) predicted probability
of success Pr(depvar=1).

pmarg2 calculates the univariate (marginal) predicted probability
of success Pr(depvar_en=1).

pcond1 calculates the conditional (on success in treatment equation)
predicted probability of success Pr(depvar=1 | depvar_en=1).

pcond2 calculates the conditional (on success in outcome equation)
predicted probability of success Pr(depvar_en=1 | depvar=1).

xb1 calculates the probit linear prediction for the outcome equation.

xb2 calculates the probit linear prediction for the treatment equation.

stdp1 calculates the standard error of the linear prediction of the outcome equation.

stdp2 calculates the standard error of the linear prediction of the treatment equation.

nooffset is relevant only if you specified offset(varname) for the
outcome equation and/or treatment equation for rbiprobit. It modifies the
calculations made by predict so that they ignore the offset variables;
the linear predictions are treated as xb1 rather than as xb1 + offset1
and xb2 rather than as xb2 + offset2

scores calculates equation-level score variables.

The first new variable will contain the derivative of the log likelihood with
respect to the first regression equation.

The second new variable will contain the derivative of the log likelihood with
respect to the second regression equation.

The third new variable will contain the derivative of the log likelihood with
respect to the third equation (atanrho).

Examples

Setup

. webuse class10

. rbiprobit graduate = income i.roommate i.hsgpagrp,
endog(program = i.campus i.scholar income i.hsgpagrp)

Predicted probability graduate = 1 and program = 1

. predict prob11

Predicted probability graduate = 1 and program = 0

. predict prob10, p10

Predicted probability graduate = 1 given program = 1

. predict grad_given_prog, pcond1

Test whether the coefficients of income are equal across equations

. test [graduate=program]: income

Test whether the coefficients on the highest category of hsgpagrp are
jointly 0 across equations

. test [graduate]: 35.hsgpagrp = 0, notest

. test [program]: 35.hsgpagrp = 0, accumulate

Author

Mustafa Coban

Institute for Employment Research (Germany)

email: mustafa.coban@iab.de

github: github.com/cobanomics

webpage: mustafacoban.de

Also see

Online: help for
rbiprobit