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发现有更详细的解说
不过没有看很懂
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. Since 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 fitted 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.
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