ivprobit.hlp

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{smcl}
{* 28mar2005}{...}
{cmd:help ivprobit} {right:dialog:  {bf:{dialog ivprobit:ivprobit}}{space 15}}
{right:also see:  {help ivprobit postestimation}}
{hline}

{title:Title}

{p2colset 5 21 23 2}{...}
{p2col :{hi:[R] ivprobit} {hline 2}}Probit model with endogenous regressors{p_end}
{p2colreset}{...}


{title:Syntax}

{phang}
Maximum likelihood estimator

{p 8 17 2}
{cmd:ivprobit} {depvar} [{it:{help varlist:varlist1}}] 
{cmd:(}{it:varlist2}{cmd: = }{it:varlist_iv}{cmd:)} {ifin} {weight} [{cmd:,}
{it:{help ivprobit##mle_options:mle_options}}] 

{phang}
Two-step estimator

{p 8 17 2}
{cmd:ivprobit} {depvar} [{it:{help varlist:varlist1}}]
{cmd:(}{it:varlist2}{cmd: = }{it:varlist_iv}{cmd:)} {ifin} {bind:{weight}}
{cmd:,} {opt two:step} [{it:{help ivprobit##tse_options:tse_options}}]

{synoptset 28 tabbed}{...}
{marker mle_options}{...}
{synopthdr :mle_options}
{synoptline}
{syntab :Model}
{synopt :{opt m:le}}use conditional maximum-likelihood estimator; the default{p_end}
{synopt :{opt asis}}retain perfect predictor variables{p_end}
{synopt :{cmdab:const:raints(}{it:{help estimation options##constraints():constraints}}{cmd:)}}apply specified linear constraints{p_end}

{syntab :SE/Robust}
{synopt :{opth vce(vcetype)}}{it:vcetype} may be {opt oim}, {opt r:obust},
{opt opg}, {opt boot:strap}, or {opt jack:knife}{p_end}
{synopt :{opt r:obust}}synonym for {cmd:vce(robust)}{p_end}
{synopt :{opth cl:uster(varname)}}adjust standard errors for intragroup
correlation{p_end}

{syntab :Reporting}
{synopt :{opt l:evel(#)}}set confidence level; default is
{cmd:level(95)}{p_end}
{synopt :{opt first}}report first-stage estimates{p_end}

{syntab :Max options}
{synopt :{it:{help ivprobit##maximize_options:maximize_options}}}control the maximization process{p_end}
{synoptline}
{p2colreset}{...}

{synoptset 28 tabbed}{...}
{marker tse_options}{...}
{synopthdr :tse_options}
{synoptline}
{syntab :Model}
{p2coldent :* {opt two:step}}use Newey's two-step estimator; the default
is {opt mle}{p_end}

{syntab :SE/Robust}
{synopt :{opth vce(vcetype)}}{it:vcetype} may be {opt boot:strap}
or {opt jack:knife}{p_end}

{syntab :Reporting}
{synopt :{opt l:evel(#)}}set confidence level; default is
{cmd:level(95)}{p_end}
{synopt :{opt first}}report first-stage estimates{p_end}
{synoptline}
{p2colreset}{...}
{p 4 6 2}* {opt twostep} is required.{p_end}

{p 4 6 2}{it:depvar}, {it:varlist1}, {it:varlist2}, and {it:varlist_iv} may
contain time-series operators: see {help tsvarlist}.{p_end}
{p 4 6 2}{cmd:bootstrap}, {cmd:by}, {cmd:jackknife}, {cmd:rolling},
{cmd:statsby}, and {cmd:xi} are allowed; see {help prefix}.{p_end}
{p 4 6 2}{cmd:fweight}s, {cmd:iweight}s, and {cmd:pweight}s are allowed with
the maximum likelihood estimator.  {cmd:fweight}s are allowed with Newey's
two-step estimator.  See {help weight}.{p_end}
{p 4 6 2}See {help ivprobit postestimation} for features available after estimation.


{title:Description}

{pstd}
{cmd:ivprobit} fits probit models where one or more of the regressors are
endogenously determined.  By default, {cmd:ivprobit} uses maximum likelihood
estimation.  Alternatively, Newey's minimum chi-squared estimator can
be invoked with the {opt twostep} option.  See {helpb ivtobit} for tobit
estimation with endogenous regressors and {helpb probit} for probit estimation
when the model contains no endogenous regressors.


{title:Options}

{dlgtab:Model}

{phang}
{opt mle} requests that the conditional maximum-likelihood estimator be
used.  This is the default.

{phang}
{opt twostep} requests that Newey's efficient two-step estimator
be used to obtain the coefficient estimates.

{phang}
{opt asis} requests that all specified variables and observations be retained
in the maximization process.  This option is typically not used and may
introduce numerical instability.  Normally {cmd:ivprobit} drops any endogenous
or exogenous variables that perfectly predict success or failure in the
dependent variable.  The associated observations are also dropped.  For more
information, see the discussion of model identification in {helpb probit}.

{phang}
{opt constraints(constraints)}; see {help estimation options}.

{dlgtab:SE/Robust}

{phang}
{opt vce(vcetype)}; see {it:{help vce_option}}.

{phang}
{opt robust}, {opth cluster(varname)}; see {help estimation options}.

{dlgtab:Reporting}

{phang}
{opt level(#)}; see {help estimation options}.

{phang}
{opt first} requests that the parameters for the reduced-form equations
showing the relationships between the endogenous variables and instruments be
displayed.  For the two-step estimator, {opt first} shows the first-stage
regressions.  For the maximum likelihood estimator, these parameters are
estimated jointly with the parameters of the probit equation.  The default is
not to show these parameter estimates.

{marker maximize_options}{...}
{dlgtab:Max options}

{phang}
{it:maximize_options}: {opt dif:ficult}, {opt tech:nique(algorithm_spec)},
{opt iter:ate(#)}, [{cmdab:no:}]{opt lo:g}, {opt tr:ace}, {opt hess:ian},
{opt grad:ient}, {opt showstep}, {opt tol:erance(#)}, {opt ltol:erance(#)},
{opt gtol:erance(#)}, {opt nrtol:erance(#)}, {opt nonrtol:erance},
{opt from(init_specs)}; see {help maximize}.  This model's likelihood function
can be difficult to maximize, especially with multiple endogenous variables.
The {opt difficult} and {cmd:technique(bfgs)} options may be helpful in
achieving convergence.


{title:Examples}

{phang}{cmd:. ivprobit fem_work fem_educ kids (other_inc = male_educ)}

{phang}{cmd:. ivprobit fem_work fem_educ kids (other_inc = male_educ), twostep}


{title:Also see}

{psee}
Manual:  {bf:[R] ivprobit}

{psee}
Online:  {help ivprobit postestimation};{break}
{helpb constraint}, {helpb heckman}, {helpb ivtobit}, {helpb oprobit}, 
{helpb regress}, {helpb tobit}, {helpb xtprobit}
{p_end}