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

Latent Factor Ordered Probit Switching Regression

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

Syntax

switchoprobitsim
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; the default is 100

facdensity(string) density of the latent factor distribution; the default is normal

mixpi(integer) specifies the mixing proportion for 2-component mixture of normals: see below

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) scale of the latent factor; also scale of mixing distribution if mixture is chosen for the density

facmean(real) specifies mean of latent factor distribution; particularly useful with gamma distribution; also specifies the mean of the
second component of the mixing distribution if mixture is chosen; see below

sesim(real) specifies number of draws of parameter vector to use in computing standard errors of ATE and ATT

cluster(varname) cluster standard errors using varname

robust compute robust variance covariance matrix

pweight, iweight, aweights are allowed; see weight

Description

switchoprobitsim 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 distinct. (A test for the hypothesis that the treated and untreated groups
belong to a single outcome regime are reported as part of standard output.) Parameters of the model are estimated by maximum simulated likelihood.

Options

Main

facdensity the density of the latent factor; the default is normal; other options are uniform,logit, chi2, lognormal and gamma. mixture
can produces a density that is a two-component mixture of normals, with the option mixpi (integer) giving the mixing proportion of a normal (0,1); facmean and
facscale specify the mean and scale of the second component; the default value for mixpi is 50.

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 standard deviation 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

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

. switchoprobitsim 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