{smcl} {* *! version 1 15March2018}{...} {cmd:help heckman_fixedrho} {hline} {title:Title} {p2colset 5 21 23 2}{...} {p2col :{cmd:heckman_fixedrho} {hline 2}}A linear regression with sample selection that allows the user to specify the value of "rho"{p_end} {p2colreset}{...} {title:Syntax} {p 8 17 2} {cmd:heckman_fixedrho} {depvar} {indepvars} {ifin} {cmd:,} {opt sel:ect}{cmd:(}{it:depvar_s} {cmd:=} {it:varlist_s} [{cmd:,} {opth off:set(varname)} {opt nocon:stant}]{cmd:)} {opt rho}{cmd:(}{it:rho_value}{cmd:)} [{it:options}] {synoptset 28 tabbed}{...} {synopthdr} {synoptline} {syntab :Model} {p2coldent :* {opt sel:ect()}}specify selection equation: dependent and independent variables; whether to have constant term and offset variable{p_end} {p2coldent :* {opt rho(#)}}specify the value of "rho" to be used{p_end} {syntab :SE/Robust} {synopt :{opth vce(vcetype)}}{it:vcetype} may be {opt oim}, {opt r:obust}, {opt cl:uster} {it:clustvar}, {cmd:opg}, {opt boot:strap}, or {opt jack:knife}{p_end} {syntab :Reporting} {synopt :{opt level(#)}}set confidence level; default is {cmd:level(95)}{p_end} {syntab :Maximization} {synopt :{it:{help heckman_fixedrho##maximize_options:maximize_options}}}control the maximization process; seldom used{p_end} {synoptline} {p2colreset}{...} {p 4 6 2} * {opt select()} and {opt rho()} are required. Note that, unlike {cmd:heckman}, {it:depvar_s} = must be specified. {p 4 6 2}{cmd:bootstrap}, {cmd:by}, {cmd:jackknife}, {cmd:rolling}, {cmd:statsby}, and {cmd:svy} are allowed; see {help prefix}.{p_end} {p 4 6 2} {opt vce()}, {opt first}, {title:Description} {pstd} {cmd:heckman_fixedrho} This is a modification of Stata's {cmd:heckman} that allows the user to specify the value of "rho," the correlation between the unobservables. For more details on {cmd:heckman}, see {manhelp heckman R}. {title:Options} {dlgtab:Model} {phang} {opt select(...)} specifies the variables and options for the selection equation. It is an integral part of specifying a selection model and is required. {pmore} {it:depvar_s} should be coded as 0 or 1, 0 indicating an observation not selected and 1 indicating a selected observation. {phang} {opt 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. {dlgtab:SE/Robust} INCLUDE help vce_asymptall {dlgtab:Reporting} {phang} {opt level(#)}; see {helpb estimation options##level():[R] estimation options}. {marker maximize_options}{...} {dlgtab:Maximization} {phang} {it:maximize_options}: {opt dif:ficult}, {opt tech:nique(algorithm_spec)}, {opt iter:ate(#)}, [{cmdab:no:}]{opt lo:g}, {opt tr:ace}, {opt grad:ient}, {opt showstep}, {opt hess:ian}, {opt showtol:erance}, {opt tol:erance(#)}, {opt ltol:erance(#)}, {opt nrtol:erance(#)}, {opt nonrtol:erance}, {opt from(init_specs)}; see {manhelp maximize R}. These options are seldom used. {title:Example} {pstd}Setup{p_end} {phang2}{cmd:. use http://fmwww.bc.edu/ec-p/data/wooldridge/mroz}{p_end} {phang2}{cmd:. gen agesq = age^2}{p_end} {phang2}{cmd:. gen child = kidslt6 + kidsge6}{p_end} {pstd}Fit a regression with sample selection and specify the value of rho to be -.7{p_end} {phang2}{cmd:. heckman_fixedrho lwage educ exper expersq city,sel(inlf= age agesq nwifeinc child educ) rho(-.7)}{p_end} {pstd}Compare with the output from {cmd:heckman}{p_end} {phang2}{cmd:. heckman lwage educ exper expersq city,sel(inlf= age agesq faminc child educ)}{p_end} {phang2}{cmd:. heckman_fixedrho lwage educ exper expersq city,sel(inlf= age agesq nwifeinc child educ) rho(-.8)} {p_end} {pstd}Technical note: Even when {cmd:heckman_fixedrho} is provided with the value of "rho" found by {cmd:heckman}, the results may differ between these two commands. The difference is due to the maximization procedures.{p_end} {title:Saved results} {pstd} {cmd:heckman_fixedrho} saves the following in {cmd:e()}: {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Scalars}{p_end} {synopt:{cmd:e(N)}}number of observations{p_end} {synopt:{cmd:e(k)}}number of parameters{p_end} {synopt:{cmd:e(df_m)}}model degrees of freedom{p_end} {synopt:{cmd:e(ll)}}log likelihood{p_end} {synopt:{cmd:e(rank)}}rank of {cmd:e(V)}{p_end} {synopt:{cmd:e(ic)}}number of iterations{p_end} {synopt:{cmd:e(rc)}}return code{p_end} {synopt:{cmd:e(converged)}}{cmd:1} if converged, {cmd:0} otherwise{p_end} {title:Author} Jonathan Cook, jacook@uci.edu {title:References} {phang} Cook, J., N. Newberger, and J. Lee. 2020. On identification and estimation of Heckman models. {it:Working paper}. {browse "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3639727":https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3639727} {phang} Cook, J., N. Newberger, and J. Lee. 2021. On identification and estimation of Heckman models. {it:Stata Journal}, 21(4), p 972-998. {browse "https://doi.org/10.1177/1536867X211063149":https://doi.org/10.1177/1536867X211063149}