02-16-2018 08:32 AM
I'm trying to do a monte carlo simulation where I need to sum over the total data set as a last step and can't seem to make it work, let alone do it in a nice way.
I want to simulate the variable A that is made from a number of parameters and the standard normal distribution. From this I want to sum another variable w if A fulfills a specific requirement.
My problem is firstly that i can't find a way to sum these w in the same data step as the simulation of A. Since my data set is about 10 000 rows and i want to do 500 000 simulations I can't have the table growing to 500 000 * 10 000 but rather i want the final table to have the sums of each iteration, in other words it should be 500 000 rows
Furthermore i'm interested in if there is a more elegant way of approaching this problem in terms of the functions used etc.
I'm using SAS 9.3 if its relevant.
this is what i have so far, but it is not working...
do iter = 1 to 2;
L = 0;
A = a1*rand("Normal") +
if A < b then L = L + w;
keep iter L;
02-16-2018 10:00 AM
I assume that if A >= b then L remains unchanged and that value of w is discarded?
Compute the conditional cumulative sum as you go along, but put the OUTPUT statement OUTSIDE the END of the iteration loop.