dear R experts: I have an academic question that borders on asking for consulting help, so I hope I am not too imposing. If I am, please ignore me.
My data set has 100MB data set of daily stock returns. I want to compute rolling (recursive?) betas---either bivariate or multivariate---with respect to some other data time series. Many of these regressions are "take away the first observation, add one observation at the end," which means I really have only about 30,000 unique regressions---still, quite a good number. Worse, I want to winsorize the rolling y-vector at different levels (99%&1%, 98%&2%, ...), so I want to repeat this procedure a few hundred times at different winsorization levels. The most important version of my task is bivariate regressions, which may mean that I don't even need MV overhead. I was even thinking of coding in C rather than R for speed sake, but I am now thinking that learning the intricacies of fast vector processing on x86 processors is so difficult, I would be done running in R faster before I would be done programming it. Has anyone done something like this? Any recommendations for what could help give me high-speed the I probably need for a task like this? Any thoughts? (I am right now working on getting blas-atlas to compile on my gentoo system. It just died in the compilation over something.) regards, /ivo ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
