help mtreatregalso see:  mtreatreg postestimation

Title

mtreatreg -- Multinomial treatment effects model

Syntax

mtreatreg
depvar
[indepvars]
[if] [in]
[weight],
mtreatment(depvar_mt = indepvars_mt)
simulationdraws(#)
density(densityname)
[options]

options

Model
mtreatment(string) string is specified as depvar_mt = indepvars_mt.  
It is required.

simulationdraws(#) number of simulation draws per observation.  
It is required.

density(mtreatreg##densityname:densityname) distribution of
depvar.  It is required.

prefix(string) prefix for the indicator variables created
from the multinomial treatment variable.  The default is a set of
indicator variables starting with _T.

constraint(constraint) apply specified linear constraints

SE/Robust
vce(vcetype) vcetype may be oim, robust,
opg, bootstrap or jackknife

robust synonym for vce(robust)

cluster(varname) adjust standard errors for intragroup
correlation

Reporting
verbose report mixed multinomial logit treatment and exogenous
outcome regressions.

Quasi-random numbers
facscale(#) specifies the standard deviation of each of the
quasi-random variables.  The default is 1.

startpoint(#) specifies the starting point in the
Halton sequence from which the quasi-random variates are generated.
The default is 20.

Max options
facfrom(string) specifies the starting value
for the parameter associated with the latent factor.  The default
starting value is zero.

maximize_options control the
maximization process.  These options are seldom used.

densityname

gamma Gamma

logit Logistic

negbin1 Negative Binomial-1 (constant dispersion)

negbin2 Negative Binomial-2 (mean dispersion)

normal Normal

bootstrap, by, jackknife, rolling, statsby, and
xi are allowed; see prefix.

pweights, aweights, fweights, and iweights are
allowed.

See mtreatreg postestimation for features available after estimation.

Description

mtreatreg fits models with multinomial treatments and continuous, count
and binary outcomes outcomes using maximum simulated likelihood.  The model
considers the effect of an endogenously chosen multinomial-valued treatment
on an outcome variable, conditional on two sets of independent variables.  
The outcome variable can be continuous, binary or integer-valued while the
treatment choice is assumed to follow a mixed multinomial logit distribution.  
The model is estimated using maximum simulated likelihood and the simulator
uses Halton sequences.

Options

Model

mtreatment(depvar_mt = indepvars_mt) specifies the variables
  for the treatment equation.  It is required.

simulationdraws(#) specifies the number of simulation draws
used per observation.  It is required.

density(densityname) specifies the distribution of depvar.  
See densityname.  It is
required.

prefix(string) allows the user to choose a prefix other than
_T for the indicator variables created from the multinomial treatment
variable.  The default is a set of indicator variables starting with
_T.  When mtreatreg is called, all previously created indicator
variables starting with the prefix specified in the prefix(string)
option or with _T are dropped.

noconstant, constraint(constraint), collinear; see
    estimation options.

SE/Robust

vce(vcetype); see vce_option.

robust, cluster(varname); see
   estimation options.

Reporting

verbose specifies that output from the mixed multinomial logit treatment and exogenous
outcome regressions be reported. verbose has no effect if from is
specified.

Quasi-random numbers

facscale(#) specifies the standard deviation of the
quasi-random variables.  The default is 1.

startpoint(#) specifies the starting point in the Halton sequence from
which the quasi-random variates are generated.  The default is 20.

Max options

facfrom(string)specifies the starting value for the parameter
associated with the latent factor.  The default starting value
is zero.

maximize_options:
difficult,
technique(algorithm_spec),
iterate(#),
[no]log,
trace,
gradient,
showstep,
hessian,
shownrtolerance,
tolerance(#),
ltolerance(#),
gtolerance(#),
nrtolerance(#),
nonrtolerance(#),
from(init_specs);
see maximize.  These options are seldom used.

Remarks

difficult may sometimes be a useful option if convergence is slow.

robust produces standard errors which take simulation error into account.  
It is the preferred option unless cluster(varname) is specified.

The available outcome densities and associated conditional means are

Density mtreatreg option	cond. mean

Gamma density(gamma)	exp(xb)
Logistic density(logit)	exp(xb)/(1+exp(xb))
Negative Binomial-1	density(negbin1)	exp(xb)
Negative Binomial-2	density(negbin2)	exp(xb)
Normal(Gaussian)	density(normal)	xb

Examples

. mtreatreg ycontinuous x1 x2, mtreat(d=x1 x2 z) sim(200) dens(gamma)

. mtreatreg yinteger x1 x2, mtreat(d=x1 x2 z) sim(200) dens(negbin1)

References

Deb, P., and P. K. Trivedi (2006), Specification and Simulated Likelihood
Estimation of a Non-normal Treatment-outcome Model with Selection: Application
to Health Care Utilization, Econometrics Journal, 9, 307-331.

Deb, P., and P. K. Trivedi (2006), Maximum Simulated Likelihood Estimation
of a Negative-binomial Regression Model with Multinomial Endogenous Treatment,
The Stata Journal, 6, 246-255.

Author

Partha Deb, Hunter College and The Graduate Center, City University of New York,
and NBER, USA.

partha.deb@hunter.cuny.edu

Also see

Online:  
mtreatreg postestimation

treatreg