Being a sometimes lazy programmer I might be tempted to add 10 variable to you data, one for each period such that period1=1 when date is in the given interval and 0 otherwise.   Something like: data aaa ; set beta.beta ; keep cusip date ret sprtrn ; format date yymmdd6. ; rename cusip=firm ret=r sprtrn=rm ; period1 = (2004 le year(date) le 2008 ); period2 = (2005 le year(date) le 2009 ); /* continue the obvious pattern*/ run ;     Then your regression could look like: proc reg data = temp outest = results ;    where period1=1;                    model r = rm ;                         by firm  ;                         quit ;   which would lend itself to a do 1 to 10 macro loop with : where period&i = 1;   I would also be tempted not to prefilter the "too few return days" but capture the n actually used by the model by adding: ods output Nobs = ObsUsed; to the Proc reg (and accumulating that as well) You might want to add a label to the model statement such as: Period&i: model .... to have some additional information about which regression things are coming from as that label will be in the OUTEST or other model related output sets.   I have to say this bit of code is not obvious as to purpose %do x = 50 %to 66 ; data temp ; set gooddata ; if &x - 49 <= n <= &x ; per = &x ; lots of magic numbers.   ... View more

1) Make a dataset (Key_Table) like : id start            end 1 2004/01/31  2008/12/31        1 2005/01/31  2009/12/31   1 2013/01/31  2017/12/31    2) Make a macro . %macro estim (id=,start=,end=);                proc reg data =have(where=(id=&id and date between &start and &end)) outest = results ;                     model r = rm ;                            by firm per ;                            quit ;              proc append base = betas1 data = results ; %end ; %mend estim ;   3) CALL EXECUTE() to go through.   data _null_;  set Key_Table;  call execute(cats('%estim (id=',id,',start=',start,',end=',end,')')); run;   ... View more