help etregress_fixedrho

Title

etregress_fixedrho -- Linear regression with endogenous treatment effects that allows the user to specify the value of "rho"

Syntax

etregress_fixedrho
depvar_1 varlist_1 [if] [in]
, treat(depvar_2 = varlist_2) rho(rho_value) [etregress_fixedrho_options]

etregress_fixedrho_options

Main
* treat() specify treatment probit: dependent and independent variables

* rho() specify the correlation between the unobservables

poutcomes use potential-outcome model with separate
treatment and control group variance

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

* Both treat() and rho() are required. The full specification for treat() is

treat(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

etregress_fixedrho Linear regression with endogenous treatment effects that allows the user to specify the value of "rho". This command is based on the full maximum likelihood version of etregress. For more details on etregress,
see [R] etregress.

Options

Main

treat() specifies the second equation: dependent and independent variables. This option 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.

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 union3

Estimation

. etregress_fixedrho wage age grade smsa black tenure union, treat(union = south black tenure) rho(-.5746478)

Compare with etregress (full ML estimation)

. etregress wage age grade smsa black tenure, treat(union = south black tenure)

Technical note: Even when etregress_fixedrho is provided with the value of
"rho" found by etregress, the results may differ between these two
commands. The difference is due to the maximization procedures.

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