{smcl} {* *! version 1.0.0 3jun2014}{...} {cmd: help treatoprobitsim} {right:also see: {help treatoprobitsim postestimation}} {hline} {title:Treatment Effects Latent Factor Ordered Probit Regression} {phang} {bf:treatoprobitsim} {hline 2} Estimate effect of potentially endogenous binary treatment on discrete, ordered outcome. {title:Syntax} {p 8 17 2} {cmdab:treatoprobitsim} y_ordered x_ordered {ifin} {weight} {cmd:, treat}(y_treat = x_treat) {cmd: simulations}(integer) [{it: options}] {synoptset 20 tabbed}{...} {synopthdr} {synoptline} {syntab: Main} {synopt:{opt sim:ulations(integer)}} number of draws from the distribution of the latent factor {p_end} {synopt:{opt facdens:ity(string)}} density of the latent factor distribution {p_end} {synopt:{opt facsk:ew(integer)}} specifies skewness of latent factor, for use with {cmd:chi2} distribution {p_end} {synopt:{opt start:point(integer)}} specifies the starting point of the draws from halton sequence used to draw from latent factor distribution {p_end} {synopt:{opt facsc:ale(real)}} standard deviation of the latent factor {p_end} {synopt:{opt facmean(real)}} specifies mean of latent factor distribution; particularly useful with {cmd: gamma} distribution {p_end} {synopt:{opt sesim(real)}} specifies the number of draws of the parameter vector are used in computing standard errors of ATE and ATT; default is 500 {p_end} {synopt:{opt c:luster(varname)}} cluster standard errors using {it:varname} {p_end} {synopt:{opt r:obust}} compute robust variance covariance matrix {p_end} {synoptline} {p 4 6 2} {cmd: pweight, iweight, aweight}s are allowed; see {help weight} {title:Description} {pstd} {cmd:treatoprobitsim} estimates a model in which {cmd:treat} is a binary indicator for a treatment ({it:y_treat}) for which selection is believed correlated with the outcome of interest, {it: y_ordered}. The model assumes that the unobservables in treatment and outcome equations follow the distribution specified in {cmd:facdensity}, and that outcomes for treated and untreated groups are not distinct. Parameters of the model are estimated by maximum likelihood. {title:Options} {dlgtab:Main} {phang} {opt facdens:ity} the density of the latent factor; the default is {cmd:normal}; other options are {cmd:uniform},{cmd:logit}, {cmd:chi2}, {cmd:lognormal}, and {cmd:gamma}. {phang} {opt facsk:ew} specifies skewness of latent factor distribution, for use with {cmd:chi2}; the default is {cmd:facskew(2)}. {phang} {opt start:point} specifies the starting point for the Halton sequence draws that are used to simulate the latent factor distribution; the default is {cmd: startpoint(5)}. {phang} {opt facsc:ale} specifies the scale of the latent factor distribution; the default is {cmd: facscale(1)}. {phang} {opt facmean} specifies the mean of the latent factor distribution; all of the distributions are normalized to be mean zero, so this parameter essentially only effects the skewness of the {cmd:gamma} distribution, which uses {cmd: invgammap} to simulate it. see {bf:[D] functions.} {title:Examples} {phang} Let self assessed health {bf: SAH} be ordered on a 1-5 scale (excellent, very good, good, fair, poor), and {bf:medicaid} be an indicator of participation in Medicaid: {p_end} {cmd:. use nhisdataex, clear} {phang}{cmd:. treatoprobitsim sah female married, treat(medicaid=female married) sim(200) facdens(logit) robust} {phang}{cmd:. treatoprobitsim sah female married [pweight=weight], treat(medicaid=female married) sim(200) facdens(logit) vce(robust)} {smcl} {title:Author} {pstd} Christian A. Gregory, Economic Research Service, USDA, cgregory@ers.usda.gov