help biprobit_fixedrho

Title

biprobit_fixedrho -- Bivariate probit regression that allows the user to specify the value of "rho"

Syntax

biprobit_fixedrho
depvar_1 varlist_1 [if] [in]
, eq2(depvar_2 = varlist_2) [biprobit_fixedrho_options]

biprobit_fixedrho_options

Main
* eq2() specify second probit: dependent and independent variables

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

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

Maximization
maximize_options control the maximization process

* eq2() is required. The full specification is

eq2(depvar_s = varlist_s)

indepvars may contain factor variables; see fvvarlist.

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

bootstrap, by, jackknife, nestreg,
rolling, statsby, stepwise, and svy
are allowed; see prefix.

Description

biprobit_fixedrho Bivariate probit regression that allows the user to specify the value of "rho". This command is based on biprobit. For more details on biprobit, see [R] biprobit.

Options

Main

eq2() specify the second equation: dependent and independent variables. This option is required.

level(#); see
[R] estimation options.

SE/Robust

vce(vcetype) specifies the type of standard error reported, which
includes types that are derived from asymptotic theory, that are robust to
some kinds of misspecification, that allow for intragroup correlation, and
that use bootstrap or jackknife methods; see
[R] vce_option.

vce(conventional), the default, uses the conventionally derived variance
estimators for first and second part models.

Maximization

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

Example

Setup

. webuse school

Estimation

.biprobit_fixedrho private logptax loginc years vote, eq2(vote=logptax loginc years) rho(0)

Compare with biprobit

.biprobit (private = years vote) (vote=logptax loginc years)

.biprobit_fixedrho private years vote, eq2(vote=logptax loginc years) rho(-.7277168)

Note that the estimates differ because of differences in the maximization procedure

Author

Jonathan Cook, jacook@uci.edu

References

Cook, J., N. Newberger, and J. Lee. 2020. On identification and estimation of Heckman models. Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3639727

Cook, J., N. Newberger, and J. Lee. 2021. On identification and estimation of Heckman models. Stata Journal, 21(4), p 972-998. https://doi.org/10.1177/1536867X211063149