May I ask what you will do with this additional column that contains the average? Are you trying to find out how far above or below the average the value of variable VALUE is? Or do you have some other reason for doing this, if so, what? (Really, explain the reason why you want this; explaining the reason should always be a part of your original post, we shouldn't have to ask)   If you are trying to find out how far above or below the average the value of variable VALUE is, then use PROC STDIZE, no extra column needed.   proc stdize data=have method=mean out=want; var value; by year; run;   Also, we have in the past asked you to not provide data as screen captures, but as working SAS data step code. Please do that in the future. ... View more

@victory wrote: The question is which is the best model to explain these two highest/lowest alpha portfolio returns from 2011 to 2015? (Note: you should judge the model performance by goodness-of-fit and parameter significance, not by alpha alone. Since some of these models are nested, try to use F-tests to compare them while you can.).          The question is about examine whether portfolio alpha is still significant with more factors. I have no idea about, someone told me the output is like that. Using four model to deal with it probably.  I don't see any "goodness-of-fit" statistics. Depending on the procedure you are running to create the "model" different fit statistics are often available. Where are the suggested F-tests? I'm not even sure that I understand what your "alpha" may be. In many modeling discussions alpha is a set level of interest for a significance test, such as alpha=0.05. So typically alpha is not dependent on or the result of a model . ... View more

Seems simple enough.  What out for testing for negative.  Missing values are considered smaller than any actual value. Also you did not say which group should include the zeros, so I made them their own group.  You did not say whether or not you wanted NOBS to count the missing values or not.  I decided to exclude them. data have; input date :yymmdd. mist ; cards; 1996-06-20 -0.0027 1996-06-21 0.0026 1996-06-24 0.0042 1996-06-25 0.0045 1996-06-26 0.0018 1996-06-27 0.0016 1996-06-28 -0.0002 1996-07-01 0.0027 1996-07-02 0.0009 1996-07-03 -0.0005 1996-07-04 0.0070 ; proc sql ; create table want as select year(date) as year , mean(mist) as mean , std(mist) as sd , sum(mist>0) as positive , sum(not (mist>=0)) as negative , mean(case when (mist>0) then mist end) as mean_postive , mean(case when (mist>0) then . else mist end) as mean_negative , count(mist) as nobs , sum(mist=0) as zero , nmiss(mist) as missing from have group by year ; quit; proc print; run;   ... View more