# Time Series: correcting the standard errors in a huge panel time series data set

I have stock returns at every 5 minute interval of each trading day for over 2 years for 40 stocks. I want to run a Fama-Macbeth regression by time interval (5min intervals) and then correct the standard errors correlation using Newey-West in SAS. This is my code:

ods listing close;
ods output parameterestimates=pe;
proc reg data=dset;
by time_interval;
model depvar = indvars; run;
quit;
ods listing;

proc means data=pe mean std t probt;
var estimate; class variable;
run;

proc sort data=pe; by variable; run;

%let lags=1000;
ods output parameterestimates=nw;
ods listing close;
proc model data=pe;
by variable;
instruments / intonly;
estimate=a;
fit estimate / gmm kernel=(bart,%eval(&lags+1),0); run;
quit;
ods listing;


As you might have I guessed, this is very computationally intensive. I can't get any results. Should I run instead the Fama-Macbeth regression by stock rather than time interval? Would I get the same thing?

Is it because I am using ods listings?

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