stata F值缺失如何解决-经管之家官网!

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stata F值缺失如何解决

stata F值缺失如何解决

发布:苏小王 | 分类:会计库






用stata进行回归分析时遇到f值缺失的问题,不知道原因是什么,请哪位大神帮忙解释一下,拜托了TheForchi2modelstatistichasbeenreportedasmissingYourestimationresultsshowanForchi2modelstatisticreportedtobemiss ...

The F or chi2 model statistic has been reported as missing
Your estimation results show an F or chi2 model statistic reported to be
missing.Stata has done that so as to not be misleading, not because
there is something necessarily wrong with your model.
Are any standard errors missing?
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.
Are you using bootstrap or jackknife?
The VCE you have just estimated is not of sufficient rank to perform the
model test.This is most likely due to not having enough replications.
The bootstrap command has a reps(#) option, and if # is less than the
number of coefficients in the model, the VCE will have insufficient rank.
The solution is to rerun bootstrap with a much larger number of
The 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 cluster() option to jackknife.
Are you using a svy estimator or did you specify the vce(cluster clustvar) opti
> on?
The VCE you have just estimated is not of sufficient rank to perform the
model test.As discussed in [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 PSUs minus
the number of strata.Because the rank of the VCE is at most d and the
model test reserves 1 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.
To simplify the remaining discussion, let's consider the case of
clustered data.This discussion applies to survey estimation in general
by substituting, "PSUs - strata" for "clusters".
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.
That concern aside, the model test statistic issue is that you cannot
simultaneously test that all coefficients are zero because there is not
enough information.You could test a subset, but not all, and so Stata
refuses to report the overall model test statistic.
Here 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 vce(robust)
and vce(cluster clustvar) options (or, for the survey commands, using the
corresponding non-svy estimator).In any case, the 6 reported is the
maximum number of coefficients that could be simultaneously tested.
Is there a regressor that is nonzero for only 1 observation or for one cluster?
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 1 observation in the estimation sample.Likewise, it
can happen if a variable is nonzero for only one cluster when using the
cluster-robust VCE.In such cases 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.Because the OPG variance matrix is
used in computing the robust variance matrix, the latter is therefore
singular as well.



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