Hi all, I want to monitor the loop process to know to what extent has the loop finished. For example, I have to do a 10000 times loop, and the approximately time is around 5~8 hours, I want to periodically check  how many times has the loop finished. I see the blog posted by Rick Wicklin "Using the MOD function as a debugging tool", proc iml; do i = 1 to 100;   x = sum(1:i);   if mod(i,10)=0 then do; /* print every 10 iterations */   print i x;   end; end; I want to do something like that, but the problem is that it only prints i and x after it is entirely finished. Is there any method which can print the i periodically during the processing?  Thanks! ... View more

Hi all, I met a problem which really bothers me a lot. here is my data set: ID    Date    Shares 1     1991     70  1     1991     65     1     1991     62 1     1992     80 1     1992     79 1     1992     69 2     1991     55 2     1991     52 2     1991     44 2     1992     85 3     1991     78 3     1991     76 3     1992     59 I want to extract the TOP 2 observations WITHIN in each ID&Date group, so below is my desired data set ID     Date     Shares 1     1991     70 1     1991     65 1     1992     80 1     1992     79 2     1991     55 2     1991     52 2     1992     85 3     1991     78 3     1991     76 3     1992     59 I was thinking to give each observation within each ID&Date group a number starting from 1 to number of observation of this subgroup, then I can delete the observation with number larger than 2. But unfortunately I failed...Does anyone can help me to figure out how to do that? I really appreciate!!! ... View more

Dear all, I want to compute the skewness of portfolio. By reading loads of literature, I found that the formula of computing the skewness of portfolio is as following: skewness_p=ω' M3 (ω⊗ω) where M3=E[(r-μ) (r-μ)'⊗(r-μ)']={aijk} the ⊗ denote the kronecker product, M3 is the co-skewness matrix, and r and u are the return matrix and average return matrix, respectively. However, I don't know how to decompose the expectation of the co-skewness matrix M3, thus I found a alternative way to calculate the M3, that's: The co-skewness matrix of M3 of dimensions (N,N2) can be represented by N Aijk matrixes (N,N) such that: M3=[ A1jk A1jk ... ANjk ] where j,k=1,…,N as well as for an index i. The individual elements of the matrix can be obtained as: the capital K denote the element in the TxN return matrix, T is the number of observations and N is the number of assets. According to the formula, I write down the following code: proc iml; T=j(81,100,.); /* This is my return matrix, for example*/ W=j(1,100,.); /* This is my weights matrix, for example*/ call randgen(T,'normal'); call randgen(W,'normal'); n=ncol(T); r=nrow(T); M=j(n,n,.); do i=1 to n;     S=j(n,n,.);        do j=1 to n;            do k=1 to n;                u=0;                      do y=1 to r-1;                       u=u+((T[y,i]-T[:,i])*(T[y,j]-T[:,j])*(T[y,k]-T[:,k]));                  end;               s[j,k]=u/(y-1);             end;        end;   M=M||S; end; M3=M[,(n+1):(n*n+n)]; skew=W*M3*(W`@W`); print skew ; quit; My problem comes out...There are 4 loops in this piece of code, and the speed is extremely low! I made a few simulation to approximate the time elapse. If I have 20 assets(n=20) in my portfolio, it will cost me 8 seconds, if I have 100 assets in my portfolio, it will cost me around 4 minutes. If I have 200 asset in my portfolio, it costs me 21 minutes! For the number of assets equal to 300, I run approximately 60 minutes and it still hasn't finished, so I have to abort it...The problem becomes unmanageable, I have 185,000 portfolios need to  be evaluated, and around 17000 of them have more than 200 assets(n>=200). Within these 17000 portfolio, there are even more than 1200 portfolios with more than 1000 assets!  This problem really makes me desperate! Does anyone know how can I improve this code? I reaaaaally reaaaally appreciate!!! ... View more