Hi everyone, I am doing an event study where I have over 5500 events. I would like to apply these events to all US firms from 1990 to 2019 period. The return data file for that period is about 1.3 gigabytes. As you can understand, this creating a massive problem with the file size. Therefore, I would like to create a macro that can do the following commands for one-event at a time and store the data.   proc sql; create table ret_data1 as select * from ret_data,eventdate;   /* it should select one event at a time */ quit;   data ret_data1; set ret_data1; before= DATE< EVTDATE;   proc sort data= ret_data1; by PERMNO EVTDATE DATE;   proc means data= ret_data1 noprint; by PERMNO EVTDATE; output out=norets (drop=_type_ _freq_) sum(before)=_bef_sum;   data ret_data2; merge ret_data1(drop=before) norets; by PERMNO EVTDATE; if first.EVTDATE then calday=-_bef_sum-1; calday+1; run;   data ret_data2; set ret_data2; drop _bef_sum; run;   data ret_data_11; set ret_data2; if -1<=calday<=1; run;   proc sql; drop table ret_data1 ret_data2 norets; quit;   Once all events are done, I would like to compile them in one file. Can someone please help me with this? Thanks in advance for your help.   I have attached the sample data to test the results.   ... View more

Hi, I am trying to calculate the Probability of Informed Trading (PIN) using the codes provided by WRDS. However, i am getting some warnings and error messages in running the codes. Here are samples of warnings and error messages:   ERROR: Execution Errors for _OBS_= 209 : ERROR: There are references to missing variables when the program code is executed for _OBS_= 209 WARNING: Your program statements cannot be executed completely. WARNING: In a total of 312 calls an error occurred during execution of the program statements. NLP attempted to recover by using a shorter step size.   The codes that i am using are the followings:   options ls=132 ps=max nocenter nodate nonumber fullstimer;   proc sort data=buysell; by RIC; run;   /*--------------------------------------------------------------------*/ /* This is the procedure used to maximize the log-likelihood function specified below. */ /* This particular factorization of the log-likelihood function appears in the paper Easley, David, Robert F. Engle, Maureen O'Hara, and Liuren Wu, 2001, "Time-Varying Arrival Rates of Informed and Uninformed Trades", AFA 2002 Atlanta Meetings, Available at SSRN: http://ssrn.com/abstract=294870 */ /*--------------------------------------------------------------------*/   proc nlp data=buysell noprint out=work.pinest (keep = RIC ALPHA ETA MU); by RIC; max loglik; decvar ALPHA, DELTA, ETA, MU; M = min(buys,sells) + max(buys,sells) / 2.0 ; x = eta / (mu + eta) ; bounds 0.0 < eta mu, 0.0 <= alpha delta <= 1.0 ;   loglik = -2.0 * eta + M * log(x) + (buys + sells) * log(mu + eta) + log( alpha * (1.0 - delta) * exp(-1.0 * mu) * (x ** (sells - M )) + alpha * delta * exp(-1.0 * mu) * (x ** (buys - M)) + (1.0 - alpha) * (x ** (buys + sells - M)) ) ;   run;   proc sort data = work.pinest nodupkey; by RIC; run;   data work.pinest; set work.pinest; PIN = alpha * mu / (alpha * mu + 2.0 * eta) ; label pin = 'Probability of informed trading' ; format pin 5.3 ; run;   I have no clues how to resolve the issue. Can someone please help? I have added sample data in the attached data section. Thanks. ... View more