svy_heckman_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}
{* 29mar2005}{...}
{cmd:help svy: heckman postestimation}{...}
{right:dialogs:  {bf:{dialog svy_heckman_p:predict}}{space 5}}
{right:also see:  {helpb "svy: heckman"}}
{hline}

{title:Title}

{p2colset 5 42 44 2}{...}
{p2col :{hi:[SVY] svy: heckman postestimation} {hline 2}}Postestimation
tools for {cmd:svy: heckman}{p_end}
{p2colreset}{...}


{title:Description}

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

INCLUDE help svy_postestimation


{title:Syntax for predict}

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

{p 8 16 2}
{cmd:predict} {dtype} {c -(}
	{it:stub}{cmd:*} |
	{newvar:_reg} {newvar:_sel} {newvar:_athrho} {newvar:_lnsigma}
	{c )-}
	{ifin} {cmd:,} {opt sc:ores}

{synoptset 13 tabbed}{...}
{synopthdr:statistic}
{synoptline}
{syntab: Main}
{synopt :{cmd:xb}}fitted values for regression equation; the
	default{p_end}
{synopt :{opt stdp}}standard error of the prediction{p_end}
{synopt :{opt xbs:el}}linear prediction for selection equation{p_end}
{synopt :{opt stdps:el}}standard
	error of the linear prediction for selection equation{p_end}
{synopt :{opt p:r(a,b)}}Pr(y | {it:a}<y<{it:b}){p_end}
{synopt :{opt e(a,b)}}E(y | {it:a}<y<{it:b}){p_end}
{synopt :{opt ys:tar(a,b)}}E(y*), y* = max({it:a},min(y,{it:b})){p_end}
{synopt :{opt yc:ond}}E(y | y observed){p_end}
{synopt :{opt ye:xpected}}E(y*), y taken to be 0 where unobserved{p_end}
{synopt :{opt ns:hazard}}nonselection hazard or inverse Mills' ratio{p_end}
{synopt :{opt m:ills}}synonym for {opt nshazard}{p_end}
{synopt :{opt ps:el}}Pr(y observed){p_end}
{synoptline}
{p2colreset}{...}
INCLUDE help esample

{pstd}
where {it:a} and {it:b} may be numbers or variables;
{it:a} missing {bind:({it:a} {ul:>} .)} means -infinity; and
{it:b} missing {bind:({it:b} {ul:>} .)} means infinity.


{title:Options for predict}

{dlgtab:Main}

{phang}
{opt xb}, the default, calculates the linear predictions from the
underlying regression equation.

{phang}
{opt stdp} calculates the standard error of the prediction from the
underlying regression equation.

{phang}
{opt xbsel} calculates the linear prediction for the selection
equation.

{phang}
{opt stdpsel} calculates the standard error of the linear prediction
for the selection equation.

{phang}
{opt pr(a,b)} calculates the
{bind:Pr({it:a} < xb+u_1 < {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;{break}
{cmd:pr(20,30)} calculates {bind:Pr(20 < xb+u_1 < 30)};{break}
{cmd:pr(lb,ub)} calculates {bind:Pr(lb < xb+u_1 < ub)}; and{break}
{cmd:pr(20,ub)} calculates {bind:Pr(20 < xb+u_1 < ub)}.

{pmore}
{it:a} missing {bind:({it:a} {ul:>} .)} means minus infinity;
{cmd:pr(.,30)} calculates {bind:Pr(xb+u_1 < 30)} and
{cmd:pr(lb,30)} calculates {bind:Pr(xb+u_1 < 30)} in observations for which
{bind:lb {ul:>} .} (and calculates {bind:Pr(lb < xb+u_1 < 30)} elsewhere).

{pmore}
{it:b} missing {bind:({it:b} {ul:>} .)} means plus infinity;
{cmd:pr(20,.)} calculates {bind:Pr(xb+u_1 > 20)} and
{cmd:pr(20,ub)} calculates {bind:Pr(xb+u_1 > 20)} in observations for which
{bind:ub {ul:>} .} (and calculates {bind:Pr(20 < xb+u_1 < ub)} elsewhere).

{phang}
{opt e(a,b)} calculates
{bind:E(xb+u_1 | {it:a} < xb+u_1 < {it:b})}, the expected value of y|x
conditional on y|x being in the interval ({it:a},{it:b}), which is to say, y|x
is censored.  {it:a} and {it:b} are specified as they are for {opt pr()}.

{phang}
{opt ystar(a,b)} calculates E(y*),
where {bind:y* = {it:a}} if {bind:xb+u_1 {ul:<} {it:a}}, {bind:y* = {it:b}} if
{bind:xb+u {ul:>} {it:b}}, and {bind:y* = xb+u} otherwise, which is to
say, y* is truncated.  {it:a} and {it:b} are specified as they are for
{opt pr()}.

{phang}
{opt ycond} calculates the expected value of the dependent variable conditional on the dependent variable being observed/selected; E(y | y observed).

{phang}
{opt yexpected} calculates the expected value of the dependent variable
(y*), where that value is taken to be 0 when it is expected to be unobserved;
y* = Pr(y observed) * E(y | y observed).

{pmore}
The assumption of 0 is valid for many cases where nonselection implies
non-participation (e.g., unobserved wage levels, insurance claims from those
who are uninsured, etc.) but may be inappropriate for some problems (e.g.,
unobserved disease incidence).

{phang}
{opt nshazard} and {opt mills} are synonyms, either calculates the
nonselection hazard -- what is often referred to as the inverse of the Mills'
ratio.

{phang}
{opt psel} calculates the probability of selection (or being observed):
Pr(y observed) = Pr(z_j*g + u_2j > 0).

{phang}
{opt nooffset} is relevant if you specified {opt offset()} when you fitted the
model.  It modifies the calculations made by {helpb predict} so that they
ignore the offset variable; the linear prediction is treated as xb rather than
xb + offset.

{phang}
{opt scores} calculates the equation-level score variables.

{pmore}
The first new variable will contain the derivative of the log likelihood with
respect to the regression equation.

{pmore}
The second new variable will contain the derivative of the log likelihood with
respect to the selection equation.

{pmore}
The third new variable will contain the derivative of the log likelihood with
respect to the third equation ({hi:athrho}).

{pmore}
The fourth new variable will contain the derivative of the log likelihood with
respect to the fourth equation ({hi:lnsigma}).


{title:Examples}

{phang}
{cmd:. webuse svywomenwk}
{p_end}
{phang}
{cmd:. svyset}
{p_end}
{phang}
{cmd:. svy: heckman wage educ age, select(married children educ age)}
{p_end}

{phang}
{cmd:. predict mills, mills}
{p_end}


{title:Also see}

{psee}
Manual:  {bf:[SVY] svy: heckman postestimation}

{psee}
Online:  {helpb "svy: heckman"};{break}
{helpb svy estat:estat},
{helpb estimates},
{helpb lincom},
{helpb mfx},
{helpb nlcom},
{helpb predictnl},
{helpb suest},
{helpb test},
{helpb testnl}
{p_end}