*! version 1 15March2018
help heckprobit_fixedrho

Title

heckprobit_fixedrho -- A probit model with sample selection that allows the user to specify the value of "rho"

Syntax

heckprobit_fixedrho depvar indepvars [if] [in] ,
select(depvar_s = varlist_s
[, offset(varname) noconstant])
rho(rho_value)
[options]

Model
* select() specify selection equation: dependent and independent variables; whether to have constant term and offset variable

* rho(#) specify the value of "rho" to be used

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

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

Maximization
maximize_options control the maximization process; seldom used

* select() and rho() are required. Note that, unlike heckprobit, depvar_s = must be specified.

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

vce(),
first,

Description

heckprobit_fixedrho This is a modification of Stata's heckprobit that allows the user to specify the value of "rho," the correlation between the unobservables. For more details on heckprobit, see [R] heckprobit.

Options

Model

select(...) specifies the variables and options for the
selection equation. It is an integral part of specifying a selection model
and is required.

depvar_s should be coded as 0 or 1, 0 indicating an
observation not selected and 1 indicating a selected observation.

rho(#) specifies the correlation between the unobservables in the selection and outcome
equations. It is required and must take a value between -1 and 1.

SE/Robust

Reporting

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

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

Fit a probit model with sample selection and specify the value of rho to be -.7

. heckprobit_fixedrho private years logptax, sel(vote=years loginc logptax) rho(-.7)

Compare with the output from heckprobit

. heckprobit private years logptax, sel(vote=years loginc logptax)

Saved results

heckprobit_fixedrho saves the following in e():

Scalars

e(N) number of observations

e(k) number of parameters

e(df_m) model degrees of freedom

e(ll) log likelihood

e(rank) rank of e(V)

e(ic) number of iterations

e(rc) return code

e(converged) 1 if converged, 0 otherwise

Authors

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