_robust.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}
{* 10feb2005}{...}
{cmd:help _robust}
{hline}

{title:Title}

{p2colset 5 20 22 2}{...}
{p2col :{hi:[P] _robust} {hline 2}}Robust variance estimates{p_end}
{p2colreset}{...}


{title:Syntax}

{p 8 16 2}{cmd:_robust} {varlist} {ifin} {weight}
	[{cmd:,} {cmdab:v:ariance:(}{it:matname}{cmd:)}
	{cmd:minus(}{it:#}{cmd:)}
	{opth str:ata(varname)}
	{opth psu(varname)}
	{opth cl:uster(varname)}
	{opth fpc(varname)}
	{opth sub:pop(varname)}
	{cmd:vsrs(}{it:matname}{cmd:)}
	{cmdab:srs:subpop}
	{cmdab:zero:weight}]

{phang}
{it:varlist} may contain time-series operators; see {help tsvarlist}.{p_end}
{phang}
{cmd:pweight}s, {cmd:aweight}s, {cmd:fweight}s, and {cmd:iweight}s are
allowed; see {help weight}.


{title:Description}

{pstd}
{cmd:_robust} is a programmer's command that computes a robust variance
estimator based on a {varlist} of equation-level scores and a covariance
matrix.  It produces estimators for ordinary data (each observation
independent), clustered data (data not independent within groups, but
independent across groups), and complex survey data from one stage of
stratified cluster sampling.

{pstd}
See {hi:[P] _robust} for a full description of this command.


{title:Options}

{phang}{cmd:variance(}{it:matname}{cmd:)} specifies a matrix containing the
unadjusted "covariance" matrix, i.e., the D in V=DMD.  The matrix must have
its rows and columns labeled with the appropriate corresponding variable
names, i.e., the names of the {it:x}s in xb.  if there are multiple equations,
the matrix must have equation names; see {helpb matrix rownames}.  The D is
overwritten with the robust covariance matrix V.  If {cmd:variance()} is not
specified, Stata assumes that D has been posted using {cmd:ereturn post};
{cmd:_robust} will then automatically post the robust covariance matrix V and
replace D.

{phang}{cmd:minus(}{it:#}{cmd:)} specifies k={it:#} for the multiplier n/(n-k)
of the robust variance estimators.  Stata's maximum likelihood commands use
k=1, and so does the {cmd:svy} prefix.  {cmd:regress, robust} uses, by
default, this multiplier with k equal to the number of explanatory variables
in the model, including the constant.  The default is {cmd:minus(1)}.

{phang}{cmd:strata(}{it:varname}{cmd:)} specifies the name of a variable
(numeric or string) that contains stratum identifiers.

{phang}{cmd:psu(}{it:varname}{cmd:)} specifies the name of a variable (numeric
or string) that contains identifiers for the primary sampling unit (PSU).
{cmd:psu()} and {cmd:cluster()} are synonyms; they both specify the same
thing.

{phang}{cmd:cluster(}{it:varname}{cmd:)} is a synonym for {cmd:psu()}.

{phang}{cmd:fpc(}{it:varname}{cmd:)} requests a finite population correction
for the variance estimates.  If the variable specified has values <= 1, it is
interpreted as a stratum sampling rate f_h = n_h/N_h, where n_h = number of
PSUs sampled from stratum h and N_h = total number of PSUs in the population
belonging to stratum h.  If the variable specified has values greater than 1,
it is interpreted as containing N_h.

{phang}{cmd:subpop(}{it:varname}{cmd:)} specifies that estimates be computed
for the single subpopulation defined by observations for which {it:varname}!=0
(and not missing).  This option would typically be used only with survey data;
see {bf:[SVY] subpopulation estimation}.

{phang}{cmd:vsrs(}{it:matname}{cmd:)} creates a matrix containing V_srswor, an
estimate of the variance that would have been observed had the data been
collected using simple random sampling without replacement.  This is used to compute design effects for survey data; see {help svy estat}.

{phang}{cmd:srssubpop} can only be specified if {cmd:vsrs()} and
{cmd:subpop()} are specified. {cmd:srssubpop} requests that the estimate of
SRS variance, {cmd:vsrs()}, be computed assuming sampling within a
subpopulation.  If {cmd:srssubpop} is not specified, it is computed assuming
sampling from the entire population.

{phang}{cmd:zeroweight} specifies whether observations with weights equal to
zero should be omitted from the computation.  This option does not apply to
{cmd:fweight}s; observations with 0 {cmd:fweight}s are always omitted.  If
{cmd:zeroweight} is specified, observations with zero weights are included in
the computation.  If {cmd:zeroweight} is not specified (the default),
observations with zero weights are omitted.  Including the observations with
zero weights affects the computation in that it may change the counts of PSUs
(clusters) per stratum.  Stata's {cmd:svy} prefix command includes
observations with zero weights; all other commands exclude them.  This option
is typically used only with survey data.


{title:Examples}

{phang2}{cmd:. regress mpg weight gear_ratio foreign, mse1}{p_end}
{phang2}{cmd:. matrix D = e(V)}{p_end}
{phang2}{cmd:. predict double e, residual}{p_end}
{phang2}{cmd:. _robust e, v(D) minus(4)}{p_end}
{phang2}{cmd:. matrix list D}


{title:Also see}

{psee}
Manual:  {bf:[P] _robust}

{psee}
Online:  {help estcom}, {helpb ereturn}, {helpb ml},
{helpb regress}, {helpb "svy: mean"}, {helpb "svy: regress"}
{p_end}