ivtobit_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}
{* 21mar2005}{...}
{cmd:help ivtobit postestimation}{right:dialog:  {bf:{dialog ivtobit_p:predict}}}
{right:also see:  {helpb ivtobit}}
{hline}

{title:Title}

{p2colset 5 35 37 2}{...}
{p2col :{hi:[R] ivtobit postestimation} {hline 2}}Postestimation tools for ivtobit{p_end}
{p2colreset}{...}


{title:Description}

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

{synoptset 14 tabbed}{...}
{p2coldent :command}description{p_end}
{synoptline}
INCLUDE help post_adjust1star
{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 ivtobit 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:ivtobit, 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 14 tabbed}{...}
{synopthdr :statistic}
{synoptline}
{syntab :Main}
{synopt :{opt xb}}linear prediction; the default{p_end}
{synopt :{opt p:r(a,b)}}Pr({it:a} < y < {it:b}); not available with two-step
estimator{p_end}
{synopt :{opt e(a,b)}}E(y {c |} {it:a} < y < {it:b}); not available with
two-step estimator{p_end}
{synopt :{opt ys:tar(a,b)}}E(y*), y* = max{c -(}{it:a},min(y,{it:b}){c )-}; not available with two-step estimator{p_end}
{synopt :{opt stdp}}standard error of prediction{p_end}
{synopt :{opt stdf}}standard error of forecast; not available with two-step
estimator{p_end}
{synoptline}
{p2colreset}{...}
INCLUDE help esample

INCLUDE help whereab


{title:Options for predict}

{dlgtab:Main}

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

{phang}{opt pr(a,b)} calculates the {bind:Pr({it:a} < xb + u < {it:b})}, the
probability that y|x would be observed in the interval ({it:a},{it:b}).

{pmore}
{it:a} and {it:b} may be specified as numbers or variable names; lb and
ub are variable names;{break}
{cmd:pr(20,30)} calculates {bind:Pr(20 < xb + u < 30)};{break}
{cmd:pr(lb,ub)} calculates {bind:Pr(lb < xb + u < ub)}; and{break}
{cmd:pr(20,ub)} calculates {bind:Pr(20 < xb + u < ub)}.

{pmore}
{it:a} missing {bind:({it:a} {ul:>} .)} means minus infinity;
{cmd:pr(.,30)} calculates {bind:Pr(xb + u < 30)};{break}
{cmd:pr(lb,30)} calculates {bind:Pr(xb + u < 30)} in
observations for which {bind:lb {ul:>} .}{break}
(and calculates {bind:Pr(lb < xb + u < 30)} elsewhere).

{pmore}
{it:b} missing {bind:({it:b} {ul:>} .)} means plus infinity; {cmd:pr(20,.)}
calculates {bind:Pr(xb + u > 20)}; {break}
{cmd:pr(20,ub)} calculates {bind:Pr(xb + u > 20)} in
observations for which {bind:ub {ul:>} .}{break} (and calculates
{bind:Pr(20 < xb + u < ub)} elsewhere).

{pmore}
{opt pr(a,b)} is not available with the two-step estimator.

{phang}
{cmd:e(}{it:a}{cmd:,}{it:b}{cmd:)} calculates
{bind:E(xb + u | {it:a} < xb + u < {it:b})}, the expected value of
{it:y}|x conditional on y|x being in the interval ({it:a},{it:b}), meaning
that {it:y}|x is censored.  {it:a} and {it:b} are specified as they are for
{cmd:pr()}.  {opt e(a,b)} is not available with the two-step estimator.

{phang}
{cmd:ystar(}{it:a}{cmd:,}{it:b}{cmd:)} calculates E(y*), where
{bind:y* = {it:a}} if {bind:xb + u {ul:<} {it:a}}, {bind:y* = {it:b}}
if {bind:xb + u {ul:>} {it:b}}, and {bind:y* = xb + u} otherwise,
meaning that y* is truncated.  {it:a} and {it:b} are specified as they
are for {cmd:pr()}.  {cmd:ystar(}{it:a},{it:b}{cmd:)} is not available with
the two-step estimator.

{phang}{opt stdp} calculates the standard error of the linear prediction.  It
can be thought of as the standard error of the predicted expected value or
mean for the observation's covariate pattern.  This is also referred to as the
standard error of the fitted value.

{phang}{opt stdf} calculates the standard error of the forecast, which is the
standard error of the point prediction for a single observation.  It is
commonly referred to as the standard error of the future or forecast value.
By construction, the standard errors produced by {opt stdf} are always larger
than those produced by {opt stdp}.
{opt stdf} 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, five 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 alpha;

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

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

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

{pmore2}
The first new variable will contain the first derivative of the log
likelihood with respect to the tobit 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:ivtobit} was called; and

{pmore2}
The remaining score variables will contain the first derivatives of the
log likehood with respect to s[1,1], 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:. ivreg rent pcturban (hsngval = faminc reg2-reg4)}{p_end}
{phang}{cmd:. mfx compute, predict(e(10, .)) eqlist(fem_inc)}

{phang}{cmd:. predict linpred, xb}{p_end}
{phang}{cmd:. predict ewage, ystar(0,.)}



{title:Also see}

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

{psee}
Online:  {helpb ivtobit};{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}