biprobit.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}
{* 08mar2005}{...}
{cmd:help biprobit} {right:dialogs:  {bf:{dialog biprobit_notsu:biprobit}}{space 20}}
{right:{dialog biprobit_su:seemingly unrelated biprobit}}
{right:also see:  {help biprobit postestimation}{space 5}}
{hline}

{title:Title}

{p2colset 5 21 23 2}{...}
{p2col :{hi:[R] biprobit} {hline 2}}Bivariate probit regression
{p2colreset}{...}


{title:Syntax}

{phang}Bivariate probit model

{p 8 17 2}
{cmd:biprobit}
{it:{help depvar:depvar1}} {it:depvar2}
[{varlist}]
{ifin}
{weight}
[{cmd:,}
{it:{help biprobit##options:options}}]

{phang}Seemingly unrelated bivariate probit model

{p 8 17 2}
{cmd:biprobit}
{it:equation1} {it:equation2}
{ifin}
{weight}
[{cmd:,} {it:{help biprobit##su_options:su_options}}]

{pstd}where {it:equation1} and {it:equation2} are specified as

{p 8 12 2}{cmd:(} [{it:eqname}{cmd:: }] {depvar} [{cmd:=}] [{varlist}]
		[{cmd:,} {opt nocon:stant}
                {opth off:set(varname)} ] {cmd:)}

{synoptset 28 tabbed}{...}
{marker options}{...}
{synopthdr}
{synoptline}
{syntab:Model}
{synopt :{opt nocon:stant}}suppress constant term{p_end}
{synopt :{opt par:tial}}fit partial observability model{p_end}
{synopt :{opth offset1(varname)}}offset variable for first equation{p_end}
{synopt :{opth offset2(varname)}}offset variable for second equation{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 of {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 noskip}}perform likelihood-ratio test{p_end}

{syntab:Max options}
{synopt :{it:{help biprobit##maximize_options:maximize_options}}}control the maximization process; seldom used{p_end}
{synoptline}
{p2colreset}{...}

{synoptset 28 tabbed}{...}
{marker su_options}
{synopthdr :su_options}
{synoptline}
{syntab:Model}
{synopt :{opt par:tial}}fit partial observability model{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 noskip}}perform likelihood-ratio test{p_end}

{syntab:Max options}
{synopt :{it:{help biprobit##maximize_options:maximize_options}}}control the maximization process; seldom used{p_end}
{synoptline}
{p2colreset}{...}

{p 4 6 2}{it:depvar1}, {it:depvar2}, {it:varlist}, and {it:depvar} may contain time-series operators; see {help tsvarlist}.{p_end}
{p 4 6 2}{opt bootstrap}, {opt by}, {opt jackknife}, {opt rolling},
{opt statsby}, and {opt xi} are allowed; see {help prefix}.{p_end}
{p 4 6 2}{opt pweight}s, {opt fweight}s, and {opt iweight}s are allowed; see 
{help weight}.{p_end}
{p 4 6 2}See {help biprobit postestimation} for features available after estimation.{p_end}


{title:Description}

{pstd}
{cmd:biprobit} fits maximum-likelihood two-equation probit
models{hline 2}either a bivariate probit or a seemingly unrelated probit
(limited to two equations).


{title:Options}

{dlgtab:Model}

{phang}
{opt noconstant}; see {help estimation options}.

{phang}
{opt partial} specifies that the partial observability model be fitted.  This
particular model commonly has poor convergence properties, so we recommend
that you use the {opt difficult} option if you want to fit the Poirier partial
observability model; see {helpb ml}.

{pmore}
Note that this model computes the product of the two dependent variables so
that you do not have to replace each with the product.

{phang}
{opth offset1(varname)}, {opt offset2(varname)}, {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 noskip} specifies that a full maximum-likelihood model with only a
constant for the regression equation be fitted.  This model is not
displayed but is used as the base model to compute a likelihood-ratio test for
the model test statistic displayed in the estimation header.  By default, the
overall model test statistic is an asymptotically equivalent Wald test of all
the parameters in the regression equation being zero (except the constant).
For many models, this option can substantially increase estimation time.

{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:. biprobit priv vote ptax inc yrs}

{phang}{cmd:. biprobit (priv = ptax inc yrs) (vote = ptax yrs), robust}


{title:Also see}

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

{psee}
Online:  {help biprobit postestimation};{break}
{helpb constraint}, 
{helpb heckman}, {helpb heckprob}, {helpb probit}
{p_end}