[R] recursive beta with cutoffs on large data set
ivo welch
ivowel at gmail.com
Sun Jun 15 18:50:04 CEST 2008
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
More information about the R-help
mailing list