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（1）在进行固定效应回归的时候，我的stata回归结果中没有报告F值，不知道是怎么回事？
A: 还请贴出结果，以便判断。
我点击那个F值的时候，跳出一个页面，是如下描述的：我截取其中的一点内容是。我也不知道自己数据是哪里有问题，调了好多遍，不知是哪种情况。
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) option?
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.
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.
Since the OPG variance matrix is used in computing the robust variance matrix,
the latter is therefore singular as well.
（2）进行固定效应回归的时候，我将其结果与混合回归进行比较，得到的winthin_R2大于混合回归的R2，这个是否合理？
A: 这并没有一定的标准。不知你的数据是什么内容？N和T分别是多少？
我的数据时企业的研发投入的数据，有的企业披露了，有的企业没有披露，我是从年报中搜取了那些已经公布企业研发支出的企业资料作为研究的样本的。一共是7年的数据，总共数据有2000多。
（3）面板回归的时候，对于共线性的关注是否很重要，我现在两个变量的vif已经有25了（使用collin命令测的），请问这个在面板中严重吗？
A: 通常大家会报告各个变量的相关系数矩阵，相关系数大于0.5或0.6的变量比较可疑。至于VIF，大于25应该算比较高了吧这意味着用这个变量与其他解释变量进行回归，R2高达0.96。
谢谢连老师答复。
（4）回归的时候，我想对企业规模加入一个平方项，但企业规模与规模的平方项本身是高相关的，我想将规模这个变量先标准化，再将标准化之后的企业规模进行平方。那么进行回归的时候，其他的变量要不要也进行标准化呢？
谢谢。
A: 很多研究的确是直接加入size和size的平方项，忽略了对共线性问题的考虑。不知你如何进行标准化？标准化能克服二者高度共线性的问题吗？
我理解错了。那在有平方项的情况下，就不需要考虑共线性了，是把。
非常感谢连老师的及时答复。
谢谢，祝您一切都好！
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