[R] recursive beta with cutoffs on large data set

[ivo welch]

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