stata问题

stata

• F值缺失的原因

大部分情况下，F值应当可以完整输出。然而在样本结构比较特殊的情况下，可能会出现F值缺失的情况。原因如下：
某个虚拟变量只有一个观测值为1而其他观测值为0，此时F值会缺失。比如，分样本描述性统计时发现某个group的数据在某年/某行业有缺失，那么在控制了年度/行业的情况下由于虚拟变量的观测值有缺失，就会出现F值为0的现象。

• F值缺失不足为惧
  In any case, the missing F-test is not anything to be concerned about. Unless your research goals specifically require a test of the omnibus null hypothesis that all of the coefficients in your model are zero, you don't need that F-test. And it is a very unusual research goal that requires that omnibus test.(在任何情况下，缺失的F检验是不需要担心的。除非你的研究目标特别要求对综合无效假设进行检验，即你的模型中所有的系数都是零，否则你不需要那个F检验。而且，需要进行综合检验的研究目标是非常不寻常的。)
  There is no issue of bias in your coefficients. (In fact, if you look at the results you got when you used the ordinary VCE, the coefficients are exactly the same.) And the standard errors and tests of all the individual coefficients are fine as well. Everything you see there is perfectly usable. The only issue is that your VCE matrix is not of full rank and so the number of coefficients that can be tested simultaneously is smaller than the full number of coefficients. But any tests of groups of coefficients that are small enough to produce a non-missing result are perfectly OK.(你的系数不存在偏差的问题。(事实上，如果你看一下你使用普通VCE时得到的结果，系数是完全一样的)。而且所有单个系数的标准误差和检验也都很好。你在那里看到的一切都完全可用。唯一的问题是，你的VCE矩阵不是全等级的，所以可以同时测试的系数数量比全部系数数量要少。但是，任何对系数组的测试，只要小到足以产生一个非遗漏的结果，都是完全可以的。)
• To second what Clyde Schechter said in #9, Celine Tran there is no problem with the situation that you have encountered. You are spending your time on digging into the details of a particular data configuration, and these details are not interesting at all.
  This "overall test of regression significance" is an anachronism of the past. This test used to make lots of sense when econometricians were running regressions with 10 observations and 2 non-constant regressors. Then you would like to know whether your overall regression explains anything...
  But you are running a regression with 500 observations and 30 regressors, of course your regression explains a lot. Look at your R-squares, they are on the order of magnitude of 20%, and this by the way is not any big deal of news either with 500 observations and 30 regressors.
  You should focus on testing interesting hypotheses motivated by economic theory, and these hypotheses almost never have anything to do with the "overall significance" of a kitchen sink regression with a full set of time and industry dummies. For such interesting hypotheses the rank deficiency of the estimates variance is typically not a problem. E.g., as you see the t-statistics for individual significance of your regressors are all fine.