ivprobit_postestimation.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}
{* 23mar2005}{...}
{cmd:help ivprobit postestimation}{right:dialog:  {bf:{dialog ivprobit_p:predict}} }
{right:also see:  {helpb ivprobit}}
{hline}

{title:Title}

{p2colset 5 36 38 2}{...}
{p2col :{hi:[R] ivprobit postestimation} {hline 2}}Postestimation tools for ivprobit{p_end}
{p2colreset}{...}


{title:Description}

{pstd}
The following postestimation commands are available for {cmd:ivprobit}:

{synoptset 13 tabbed}{...}
{p2coldent :command}description{p_end}
{synoptline}
{p2coldent :* {helpb adjust}}adjusted predictions of xb{p_end}
{p2coldent :+ {helpb estat}}AIC, BIC, VCE, and estimation sample summary{p_end}
INCLUDE help post_estimates
INCLUDE help post_hausman
INCLUDE help post_lincom
{synopt :{helpb lrtest}}likelihood-ratio test; not available with two-step estimator{p_end}
INCLUDE help post_mfx
INCLUDE help post_nlcom
{synopt :{helpb ivprobit postestimation##predict:predict}}predictions, residuals, influence statistics, and other diagnostic measures{p_end}
INCLUDE help post_predictnl
{p2coldent :+ {helpb suest}}seemingly unrelated estimation{p_end}
INCLUDE help post_test
INCLUDE help post_testnl
{synoptline}
{p2colreset}{...}
{p 4 6 2}
* {cmd:adjust} does not work with time-series operators.
{p_end}
{p 4 6 2}
+ {cmd:estat ic} and {cmd:suest} do not work after {cmd:ivprobit, twostep}.
{p_end}


{marker predict}{...}
{title:Syntax for predict}

{phang}
After ML or twostep

{p 8 16 2}
{cmd:predict} {dtype} {newvar} {ifin} [{cmd:,} {it:statistic}]

{phang}
After ML

{p 8 16 2}
{cmd:predict} {dtype} {it:stub*} {ifin} {cmd:,} {opt sc:ores}


{synoptset 13 tabbed}{...}
{synopthdr :statistic}
{synoptline}
{syntab :Main}
{synopt :{opt xb}}linear prediction; the default{p_end}
{synopt :{opt stdp}}standard error of the linear prediction{p_end}
{synopt :{opt p}}probability of a positive outcome; not available with two-step
estimator{p_end}
{synopt :{opt rules}}probability of a positive outcome applying any rules used
to identify the model; not available with two-step estimator{p_end}
{synopt :{opt asif}}probability of a positive outcome ignoring any rules and
exclusion criteria used to identify the model; not available with two-step
estimator{p_end}
{synoptline}
{p2colreset}{...}
INCLUDE help esample


{title:Options for predict}

{phang}{opt xb}, the default, calculates the linear prediction.

{phang}{opt stdp} calculates the standard error of linear prediction. 

{phang}{opt p} calculates the probability of a positive outcome. {opt p} is
not available with the two-step estimator.

{phang}{opt rules} requests that Stata use any ''rules'' that were used to
identify the model when making the prediction.  By default, Stata calculates
missing for excluded observations. {opt rules} is not available with the
two-step estimator.

{phang}{opt asif} requests that Stata ignore the rules and the exclusion
criteria and calculate predictions for all observations possible using the
estimated parameters from the model.  {opt asif} is not available with the
two-step estimator.

{phang}{opt scores}, not available with {opt twostep}, calculates
equation-level score variables.

{pmore}
For models with a single endogenous regressor, four new variables are 
created:

{pmore2}
The first new variable will contain the first derivative of the log 
likelihood with respect to the probit equation;

{pmore2}
The second new variable will contain the first derivative of the log 
likelihood with respect to the reduced-form equation for the endogenous 
regressor;

{pmore2}
The third new variable will contain the first derivative of the log 
likelihood with respect to atanh(rho); and

{pmore2}
The fourth new variable will contain the first derivative of the log 
likelihood with respect to ln(sigma).

{pmore}
For models with j endogenous regressors, 
j + {c -(}(j + 1)(j + 2){c )-}/2
new variables are created.

{pmore2}
The first new variable will contain the first derivative of the log 
likelihood with respect to the probit equation;

{pmore2}
The second through (j + 1)th new variables will contain the first 
derivatives of the log likelihood with respect to the reduced-form 
equations for the endogenous variables in the order they were specified 
when {cmd:ivprobit} was called; and

{pmore2}
The remaining score variables will contain the first derivatives of the
log likehood with respect to s[2,1], s[3,1], ..., s[j+1,1], s[2,2], 
..., s[j+1,2], ..., s[j+1,j+1], where s[m,n] denotes the (m,n) element 
of the Cholesky decomposition of the error covariance matrix.


{title:Examples}

{phang}{cmd:. mfx compute, predict(p) eqlist(fem_work)}


{title:Also see}

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

{psee}
Online:  {helpb ivprobit};{break}
{helpb adjust}, {helpb estimates}, {helpb hausman},
{helpb lincom}, {helpb lrtest}, {helpb mfx}, {helpb nlcom},
{helpb predictnl}, {helpb suest}, {helpb test}, {helpb testnl}
{p_end}