My first post on SAS bloggers begins with Rick Wicklin and I plan a series of posts. It would be nice for a statement of rational before following pages.
As a learner, I benefit frequently from lots of high quality blogs. But I’m also slightly lazy as a reader: I read almost all blogs in Google […]
Category: SAS
The value of nohup
I spent the last week working on a single request from a governmental oversight agencies that monitors one of the many federal contracts we have. The request involved pulling all of the data from the last four year years that met a broad criteria before running a fairly complex analysis on it. The worst part […]
Using PROC COPULA in a more volatile market
The last week witnessed one of the wildest fluctuations in the market. Copula could measure the nonlinear dependence of multiple assets in a portfolio, and most importantly, is pronounced as \`kä-pyə-lə\(Thanks to the tip by Rick). The latest COPUL…
Using PROC COPULA in a more volatile market
The last week witnessed one of the wildest fluctuations in the market. Copula could measure the nonlinear dependence of multiple assets in a portfolio, and most importantly, is pronounced as \`kä-pyə-lə\(Thanks to the tip by Rick). The latest COPUL…
Using PROC COPULA in a more volatile market
The last week witnessed one of the wildest fluctuations in the market. Copula could measure the nonlinear dependence of multiple assets in a portfolio, and most importantly, is pronounced as \`kä-pyə-lə\(Thanks to the tip by Rick). The latest COPUL…
Rolling Window Regression of Time Series
More often than not, we encounter a problem where an OLS over a rolling time window is required, see [1], [2], [3], [4], [5], [6], [7], for a few examples.
One solution is to resort to SAS MACRO, but it is extremely inefficient and can’t handle lar…
Rolling Regression of Time Series
proc datasets library=work kill; run;
options fullstimer;
data test;
do seq=1 to 500000;
x1=rannor(9347957);
*x2=rannor(876769)+0.1*x1;
epsilon=rannor(938647)*0.5;
y = 1.5 + 0.5*x1 +epsilon;
o…
Rolling Regression of Time Series
proc datasets library=work kill; run;
options fullstimer;
data test;
do seq=1 to 500000;
x1=rannor(9347957);
*x2=rannor(876769)+0.1*x1;
epsilon=rannor(938647)*0.5;
y = 1.5 + 0.5*x1 +epsilon;
o…