mprobit.hlp

是一个经济学管理应用软件 很难找的 但是经济学学生又必须用到

{smcl}
{* *! version 1.0.0  24may2005}{...}
{cmd:help mprobit} {right:dialog:  {bf:{dialog mprobit}}{space 15}}
{right:also see:  {help mprobit postestimation}}
{hline}

{title:Title}

{p2colset 5 20 22 2}{...}
{synopt :{hi:[R] mprobit} {hline 2}}Multinomial probit regression{p_end}
{p2colreset}{...}


{title:Syntax}

{p 8 13 2}
{cmd:mprobit} 
{depvar} 
[{indepvars}] 
{ifin}
{weight}{cmd:,}
[{it:options}]

{synoptset 25 tabbed}{...}
{synopthdr}
{synoptline}
{syntab :Model}
{synopt :{opt nocon:stant}}suppress constant terms{p_end}
{synopt :{opt base:outcome(#|lbl)}}outcome used for normalizing location{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}}compute standard errors using the robust/sandwich estimator{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}

{syntab :Int options}
{synopt :{opt intp:oints(#)}}number of quadrature points{p_end}

{syntab :Max options}
{synopt :{it:{help mprobit##maximize_options:maximize_options}}}control the maximization process; seldom used{p_end}
{p2line}
{p2colreset}{...}
{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; see {help weight}.{p_end}
{p 4 6 2}See {help mprobit postestimation} for features available after estimation.{p_end}

 
{title:Description}

{pstd}
{cmd:mprobit} fits multinomial probit (MNP) models via maximum 
likelihood.  {it:depvar} contains the outcome for each observation, 
and {it:indepvars} are the associated covariates.  The error terms are 
assumed to be independent, standard normal, random variables.  
See {help asmprobit:asmprobit} for the case where the latent-variable
errors are correlated or heteroskedastic and you have 
alternative-specific variables.


{title:Options}

{dlgtab:Model}

{phang}{opt noconstant} suppresses the J-1 constant terms.

{phang}{opt baseoutcome(#|lbl)} specifies the outcome that normalizes the location
of the latent variable.  
Note that the base outcome may be specified as a number or a label.
The default is to use the most frequent outcome.  
The coefficients associated with the base outcome are zero.

{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}.

{dlgtab:Int options}

{phang}{opt intpoints(#)} specifies the number of Gaussian quadrature
points to use in approximating the likelihood.  The default is 15. 


{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}.  These options are seldom used.


{title:Examples}

{phang}{cmd:. webuse sysdsn3}{p_end}

{phang}{cmd:. mprobit insure age male nonwhite site2 site3}{p_end}


{title:Also see}

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

{psee}
Online:  {help mprobit postestimation};{break}
{help estcom}, {help postest}; {helpb asmprobit}, {helpb clogit},
{helpb mlogit}, {helpb nlogit}, {helpb ologit}, {helpb slogit}
{p_end}