j_robustsingular.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}
{* 07apr2005}{...}
{title:The F or chi2 model statistic has been reported as missing}

{pstd}
Your estimation results show a F or chi2 model statistic reported to be
missing.  Stata has done that so as not to be misleading, not because
there is something necessarily wrong with your model.


{title:Are any standard errors missing?}

{pstd}
If any standard errors are reported as dots, something is wrong with your
model:  One or more coefficients could not be estimated in the normal
statistical sense.  You need to address that problem and ignore the rest of
this discussion.


{title:Are you using {cmd:bootstrap} or {cmd:jackknife}?}

{pstd}
The VCE you have just estimated is not of sufficient rank to perform the model
test.  This is most likely due to an insufficient number of replications.

{pstd}
The {cmd:bootstrap} command has a {opt reps(#)} option, and if {it:#} is less
than the number of coefficients in the model, the VCE will have insufficient
rank.  The solution is to rerun {cmd:bootstrap} with a much larger number of
replications.

{pstd}
The {cmd:jackknife} command estimates the VCE by refitting the model for each
observation in the dataset, leaving the associated observation out of the
estimation sample each time.  As with the conventional variance estimator,
the VCE will be singular if the number of observations is less than the number
of parameters.  See the following discussion if you supplied the
{opt cluster()} option to {cmd:jackknife}.


{title:Are you using a {cmd:svy} estimator or did you specify the {cmd:cluster()} option?}

{pstd}
The VCE you have just estimated is not of sufficient rank to perform the model
test.  As discussed in {hi:[R] test}, the model test with clustered or
survey data is distributed as F(k,d-k+1) or chi2(k) where k is the number of
constraints and d=number of clusters or d=number of PSU's minus the number of
strata.  Since the rank of the VCE is at most d and the model test reserves
one degree of freedom for the constant, at most d-1 constraints can be tested,
so k must be less than d.  The model that you just fit does not meet this
requirement.

{pstd}
To simplify the remaining discussion, let's consider the case of clustered
data.  This discussion applies to survey estimation in general by
substituting, "PSU's - strata" for "clusters".

{pstd}
There is no mechanical problem with your model, but you need to consider
carefully whether any of the reported standard errors mean anything.  The
theory that justifies the standard error calculation is asymptotic in the
number of clusters, and we have just established that you are estimating at
least as many parameters as you have clusters.

{pstd}
Putting that concern aside, the model test statistic issue is that you
cannot simultaneously test that all coefficients are zero because there is
insufficient information.  You could test a subset, but not all, and so
Stata refuses to report the overall model test statistic.

{pstd}
In this case, note the degrees of freedom reported for the chi2 or F.
You might see chi2(6) or F(6, 5).  If you were to count the number
of coefficients that would be constrained to 0 in a model test in this case,
you would find that number to be greater than 6.  You could find out what
that number is by reestimating the model parameters without the {cmd:robust}
and {cmd:cluster()} options (or, in the case of the {help survey} commands,
using the corresponding non {cmd:svy} estimator).  In any case, the 6
reported is the maximum number of coefficients that could be simultaneously
tested.


{title:Is there a regressor that is nonzero for only one observation?}

{pstd} 
The VCE you have just estimated is not of sufficient rank to
perform the model test.  This can happen if there is a variable in your
model that is nonzero for only a single observation in the estimation
sample.  In that case the derivative of the sum-of-squares or likelihood
function with respect to that variable's parameter is zero for all
observations.  That implies that the outer-product-of-gradients (OPG)
variance matrix is singular.  Since the OPG variance matrix is used in 
computing the robust variance matrix, the latter is therefore singular as well. 


{title:Also see}

{psee}
Manual:  {bf:[SVY] survey},{break}
{bf:[R] test},{break}
{bf:[P] _robust}

{psee}
Online:  {helpb bootstrap},
{helpb jackknife},
{help survey},
{helpb test}
{p_end}