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
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
    replications.

    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.