*! version 1.0.0 3jun2014
help treatoprobitsim
also see: treatoprobitsim postestimation

Treatment Effects Latent Factor Ordered Probit Regression

treatoprobitsim -- Estimate effect of potentially endogenous binary treatment on discrete, ordered outcome.

Syntax

treatoprobitsim
y_ordered x_ordered
[if] [in]
[weight]
, treat(y_treat = x_treat)
simulations(integer)
[ options]

Main
simulations(integer) number of draws from the distribution of the latent factor

facdensity(string) density of the latent factor distribution

facskew(integer) specifies skewness of latent factor, for use with chi2 distribution

startpoint(integer) specifies the starting point of the draws from halton sequence used to draw from latent factor distribution

facscale(real) standard deviation of the latent factor

facmean(real) specifies mean of latent factor distribution; particularly useful with gamma distribution

sesim(real) specifies the number of draws of the parameter vector are used in computing standard errors of ATE and ATT; default is 500

cluster(varname) cluster standard errors using varname

robust compute robust variance covariance matrix

pweight, iweight, aweights are allowed; see weight

Description

treatoprobitsim estimates a model in which treat is a binary indicator for a treatment (y_treat) for which selection is believed
correlated with the outcome of interest, y_ordered. The model assumes that the unobservables in treatment and outcome equations follow the distribution
specified in facdensity, and that outcomes for treated and untreated groups are not distinct. Parameters of the model are estimated by maximum likelihood.

Options

Main

facdensity the density of the latent factor; the default is normal; other options are uniform,logit, chi2, lognormal, and gamma.

facskew specifies skewness of latent factor distribution, for use with chi2; the default is facskew(2).

startpoint specifies the starting point for the Halton sequence draws that are used to simulate the latent factor distribution;
the default is startpoint(5).

facscale specifies the scale of the latent factor distribution; the default is facscale(1).

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 gamma distribution, which uses invgammap to simulate it. see [D] functions.

Examples

Let self assessed health SAH be ordered on a 1-5 scale (excellent, very good, good, fair, poor), and medicaid be an indicator of participation in Medicaid:

. use nhisdataex, clear

. treatoprobitsim sah female married, treat(medicaid=female married) sim(200) facdens(logit) robust

. treatoprobitsim sah female married [pweight=weight], treat(medicaid=female married) sim(200) facdens(logit) vce(robust)

Author

Christian A. Gregory, Economic Research Service, USDA, cgregory@ers.usda.gov