[R] rolling regression

[R. Michael Weylandt]

It seems you don't really know how predict works. If you don't supply
new data, it will only return the least squares fit to the old data,
which is the large data block you saw. Check the first example given
in ?predict to see how this works for new (out of sample) data.

More importantly, use of lm() gives a model for contemporaneous
fitting of your data to cash_ret. You probably need to use a time
series model that has forecasting built into it (unless you can
somehow your independent variables before your dependent variables)

Michael Weylandt

On Sun, Oct 2, 2011 at 11:41 PM, Darius H <xenon99 at hotmail.com> wrote:
>
> Dear all,
>
> I have spent the last few days on a seemingly simple and previously documented rolling regression.
>
> I have a 60 year data set organized in a ts matrix.
> The matrix has 5 columns; cash_ret, epy1, ism1, spread1, unemp1
>
> I have been able to come up with the following based on previous help threads. It seems to work fine.
> The trouble is I get regression coefficients but need the immediate next period forecast.
>
> cash_fit= rollapply(cash_data, width=60,
>
> function(x) coef(lm(cash_ret~epy1+ism1+spread1+unemp1, data = as.data.frame(x))),
>
> by.column=FALSE, align="right"); cash_fit
>
>
> I tried to replace "coef" above to "predict" but I get a whole bunch of results too big to be displayed. I would be grateful
> if someone could guide me on how to get the next period forecast after each regression.
>
> If there is a possibility of getting the significance of each regressor and the standard error in addition to R-sq
> without having to spend the next week, that would be helpful as well.
>
> Many thanks,
> Darius
>
>
>
>
>
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