*! version 1.1.0: 18apr2022

help rbiprobitgithub.com/cobanomics/rbiprobit

version 1.1.0

Title

rbiprobit -- Recursive bivariate probit regression

Syntax

rbiprobit depvar [=] [indepvars] [if] [in]
[weight]
,
endogenous( depvar_en [=]
[indepvars_en]
[, enopts] )
[options]

options Description

Model
noconstant suppress constant term

offset(varname) offset variable for outcome equation

constraints(constraints) apply specified linear constraints

collinear keep collinear variables

SE/Robust
vce(vcetype) vcetype may be oim,
robust, cluster clustvar, opg,
bootstrap, or jackknife

Reporting
level(#) set confidence level; default is level(95)

lrmodel perform likelihood-ratio model test instead of the default Wald test

nocnsreport do not display constraints

display_options control

Maximization
maximize_options control the maximization process; seldom used

enopts Description

Model
noconstant suppress constant term

offset(varname) offset variable for treatment equation

indepvars and indepvars_en may contain factor variables; see fvvarlist.

depvar, depvar_en, indepvars, and indepvars_en may contain
time-series operators; see tsvarlist.

bootstrap, jackknife, and svy are allowed; see prefix.

Weights are not allowed with the bootstrap prefix.

vce(), lrmodel, and weights are not allowed with the svy prefix.

pweights, fweights, and iweights are allowed; see weight.

coeflegend does not appear in the dialog box.

See rbiprobit postestimation for features available after estimation.

Description

rbiprobit is a user-written command that fits a recursive bivariate
probit regression using maximum likelihood estimation. It is
implemented as an lf1 ml evaluator. The model involves an outcome equation with
the dependent variable depvar and a treatment equation with the dependent
variable depvar_en. Both dependent variables depvar and depvar_en have to
be binary and coded as 0/1 variables.

rbiprobit automatically adds the treatment
variable depvar_en as an independent variable on the right-hand side of the outcome
equation. The independent variables in indepvars and indepvars_en may be different
or identical. rbiprobit is limited to a recursive model with two equations.

Options

Model

noconstant; see [R] estimation options.

offset(varname), constraints(constraints) and collinear; see
[R] estimation options.

SE/Robust

Reporting

level(#), lrmodel and nocnsreport; see [R] estimation options.

Maximization

maximize_options: difficult, technique(maximize##algorithm_spec:algorithm_spec),
iterate(#), [no]log, trace, gradient, showstep,
hessian, showtolerance, tolerance(#), ltolerance(#),
nrtolerance(#), nonrtolerance, and from(init_specs);
see [R] maximize. These options are seldom used.

Setting the optimization type to technique(bhhh) resets the default vcetype to vce(opg).

The following option is available with rbiprobit but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Examples

Setup

. webuse class10

Recursive bivariate probit regression

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

Recursive bivariate probit regression with robust standard errors

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

Stored results

rbiprobit stores the following in e():

Scalars

e(N) number of observations

e(k) number of parameters

e(k_eq) number of equations in e(b)

e(k_aux) number of auxiliary parameters

e(k_eq_model) number of equations in overall model test

e(k_dv) number of dependent variables

e(df_m) model degrees of freedom

e(ll) log likelihood

e(ll_0) log likelihood, constant-only model (lrmodel only)

e(ll_c) log likelihood, comparison model

e(chi2) chi-squared

e(N_clust) number of clusters

e(chi2_c) chi-squared for comparison test

e(p) p-value for model test

e(rho) rho

e(rank) rank of e(V)

e(rank0) rank of e(V) for constant-only model

e(ic) number of iterations

e(rc) return code

e(converged) 1 if converged, 0 otherwise

Macros

e(cmd) rbiprobit

e(cmdline) command as typed

e(depvar) names of dependent variables

e(wtype) weight type

e(wexp) weight expression

e(title) title in estimation output

e(clustvar) name of cluster variable

e(offset1) offset for outcome equation

e(offset2) offset for treatment equation

e(chi2type) Wald or LR; type of model chi-squared test

e(chi2_ct) Wald or LR; type of model chi-squared test corresponding to e(chi2_c)

e(vce) vcetype specified in vce()

e(vcetype) title used to label Std. Err.

e(opt) type of optimization

e(which) max or min; whether optimizer is to perform
maximization or minimization

e(ml_method) type of ml method

e(user) name of likelihood-evaluator program

e(technique) maximization technique

e(properties) b V

e(predict) program used to implement predict

e(marginsok) predictions allowed by rbiprobit margdec

e(marginsnotok) predictions disallowed by rbiprobit margdec

e(asbalanced) factor variables fvset as asbalanced

e(asobserved) factor variables fvset as asobserved

Matrices

e(b) coefficient vector

e(Cns) constraints matrix

e(ilog) iteration log (up to 20 iterations)

e(gradient) gradient vector

e(V) variance-covariance matrix of the estimators

e(V_modelbased) model-based variance

Functions

e(sample) marks estimation sample

References

Coban, M. (2020). Redistribution Preferences, Attitudes towards Immigrants, and
Ethnic Diversity, IAB Discussion Paper 2020/23. Available
from http://doku.iab.de/discussionpapers/2020/dp2320.pdf

Greene, W.H. (2018). Econometric Analysis, 8th Edition, Pearson.

Hasebe, T. (2013). Marginal effects of a bivariate binary choice model,
Economic Letters 121(2),
pp. 298-301. DOI: 10.1016/j.econlet.2013.08.028

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
biprobit, eprobit

Packages from the SSC Archive (type ssc describe name for
more information):
bicop,
bioprobit,
mvprobit