[R-SIG-Finance] repeating regression

[R. Michael Weylandt]

You can probably specify the problem with model.matrix and use lm.fit
directly, but what's probably even better is to remember that the
slope can be calculated as correlation * std_y / std_x for this simple
case of one independent variable and implement directly . E.g.,
something like

apply(Data, 1, function(x) std(x[6:10])/std(x[1:5]) * cor(x[1:5],x[6:10]))

You can do this even faster by taking x and y to big vectors, taking a
rolling std and cor with length 5, and sampling each 5 steps as well,
but it's after midnight and I'm doing a disastrous OJ project (don't
ask....) for school so I can't really think right now.

Michael

On Mon, Nov 21, 2011 at 10:52 PM, Robert A'gata <rhelpacc at gmail.com> wrote:
> Hi,
>
> I think my problem is a bit mundane but it's quite intriguing. Imagine
> I have a matrix of 10 by 2 million. The first 5 columns are x and the
> last 5 are y values. I have to regress y on x (assume 0 intercept) for
> each row to observe time series of the slope. I am wondering if there
> is any way to speed this calculation up? I tried with apply. But it is
> still slow. Is there any trick I should know? Thank you.
>
> Robert
>
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