Hi, I want to use proc sql to calculate sum of some observations group by certain variable, but subject to some constraint. Below is the detail.
I have some stocks and their returns for each day. I would like to calculate the mean of daily returns for each stock ID, but only the first few days, for example, the first two days.
ID Day Return
1 1 0.2
1 2 0.2
1 3 0.3
2 1 0.2
2 2 0.1
2 3 0.2
3 1 0.2
3 2 0.6
3 3 0.2
The program below will give full average of daily returns for each ID, but not the first two days for each ID. How to revise it?
create table want as
select *, mean(return) as avg_ret
group by ID;
proc sql; create table want as select *, mean(return) as avg_ret from from group by ID where day in (1,2) ; quit;
@Kurt_Bremser: I think the WHERE clause must precede the GROUP BY clause.
day 1 and 2 are just examples. In reality, the variable day is in terms of date and it's not necessarily continuous. I only want to use the first few observations, say, the first 10 observations in each group (ID). How can that be done?
I think it would be easier (and more efficient) to select the first n observations per ID using a data step:
/* Create test data */ data have; input ID Day Return; cards; 1 1 0.2 1 2 0.2 1 3 0.3 2 1 0.2 2 2 0.1 2 3 0.2 3 1 0.2 3 2 0.6 3 3 0.2 ; %let n=2; /* Select first (i.e. earliest) n obs. per ID */ data temp / view=temp; set have; by id day; if first.id then c=1; else c+1; if c<=&n; drop c; run;
Then you could apply your original PROC SQL step to TEMP rather than HAVE (what you called "FROM").
Alternatively, here is a pure PROC SQL approach:
proc sql; create table want as select *, mean(return) as avg_ret from (select * from have a where (select count(*) from have where id=a.id & .<day<=a.day)<=&n) group by id order by id, day; quit;
Here, TEMP is replaced by an inline view (which, in addition, involves a subquery in its WHERE clause).
It is assumed that the order of DAY values corresponds to chronological order. Unlike the data step, the PROC SQL step does not require dataset HAVE to be sorted (by ID, DAY). Furthermore, there should be no duplicate DAY values within an ID.
You might as well do it in one go.
data HAVE; input ID DAY RETURN; cards; 1 1 0.2 1 2 0.2 1 3 0.3 2 10 0.2 2 12 0.1 2 12 0.2 3 11 0.2 3 12 0.6 3 13 0.2 run; data WANT ; set HAVE; by ID; PREV_RETURN=lag(RETURN); if lag(first.ID); MEAN=mean(PREV_RETURN, RETURN); run; proc print; var ID MEAN; run;
For an arbitrary nb of obs:
data HAVE; input ID DAY RETURN; cards; 1 1 0.2 1 2 0.2 1 3 0.3 2 10 0.2 2 12 0.1 2 12 0.2 3 11 0.2 3 12 0.6 3 13 0.2 run; %let nb_obs=2; data WANT ; set HAVE; by ID; if first.ID then call missing(SUM,OBS); SUM+RETURN; OBS+1; if OBS=&nb_obs. or (OBS < &nb_obs. & last.ID) then do; MEAN=divide(SUM, OBS); output; end; run; proc print; var ID MEAN; run;
If you really must use SQL (but SQL is not suited to processing rows in a certain order), something as ugly as this works in SAS:
%let nb_obs=2; proc sql nothreads; create table WANT(keep=ID MEAN) as select a.ID , monotonic() as ROW , mean(a.RETURN) as MEAN from HAVE a ,( select ID , min(monotonic()) as GRP_START from HAVE group by ID ) b where a.ID=b.ID and calculated ROW<b.GRP_START+&nb_obs. group by a.ID ; quit;
Registration is open! SAS is returning to Vegas for an AI and analytics experience like no other! Whether you're an executive, manager, end user or SAS partner, SAS Innovate is designed for everyone on your team. Register for just $495 by 12/31/2023.
If you are interested in speaking, there is still time to submit a session idea. More details are posted on the website.
Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.
Find more tutorials on the SAS Users YouTube channel.