Good point, but I still prefer weighting to choosing a rolling window, maybe because I never had good success with them. 

A better point of view is the one of defining structural breaks as mentioned earlier and select the window up until that last break.

A note: you never reasonably know if data are not applicable to the future: you cannot make information that you do not have. 
However regime switching helps to adapt models to structural breaks and sometimes losing one prediction point is not too bad as decision can adjust bad predictions (stop loss, degearing...).

So I think you are right and wrong and we have been too generic.




________________________________
 Da: Patrick Burns <patrick@burns-stat.com>

Cc: "r-sig-finance@r-project.org" <r-sig-finance@r-project.org> 
Inviato: Giovedė 12 Gennaio 2012 9:09
Oggetto: Re: [R-SIG-Finance] dynamic window size in rolling linear regression?

Throwing away data *is* good if the
process changes and that data is not
applicable to the future.

Exponential weights throw away lots of
data -- though they leave a very small
amount of weight (at least) for all
datapoints.

Pat

On 11/01/2012 18:08, riccardo visca wrote:
> What about using an expanding window with exponential weights to make
> the coefficients more adaptive? Throwing away data is not good.
> Still you need to have weights that are inverse of conditional variance
> to correct eteroschedasticity.
>
> It could be a lot more efficient computationally than Kalman and one
> could use robust or lasso, ridge, pls...
> Food for thoughts...
>
>
> ------------------------------------------------------------------------
> *Da:* Patrick Burns <patrick@burns-stat.com>
> *A:* r-sig-finance@r-project.org
> *Inviato:* Mercoledė 11 Gennaio 2012 17:35
> *Oggetto:* Re: [R-SIG-Finance] dynamic window size in rolling linear
> regression?
>
> Let's think about what you are asking for.
>
> You want to change the window size in order
> (I presume) to get better predictions. So
> it seems to me that you would need a variable
> that has information about the pertinence of
> past data to the future.
>
> I could imagine volatility being such a variable
> in some circumstances. I don't know of any
> work along those lines -- I'd be interested to
> hear of any.
>
> My usual practice is to have weights that descend
> linearly. In comparison to exponentially decaying
> weights this puts more weight on the older data,
> and hence is often a more stable estimate. It has
> the advantage over equal weighting that the window
> size is of less importance.
>
> On 11/01/2012 17:11, Michael wrote:
>  > Hi all,
>  >
>  > In application of linear regression to financial time series, we always
>  > have a parameter which is the window size.
>  >
>  > It's clear that a lot of results are sensitive to this parameter...
>  >
>  > Is there a way to make this parameter dynamic, or are there statistical
>  > procedures to select such parameter dynamically and/or "optimally"?
>  >
>  > From a trading strategy perspective, is there a way to make this
> parameter
>  > dynamically chosen?
>  >
>  > Thanks a lot!
>  >
>  > [[alternative HTML version deleted]]
>  >
>  > _______________________________________________
>  > R-SIG-Finance@r-project.org <mailto:R-SIG-Finance@r-project.org>
> mailing list
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>  > -- Subscriber-posting only. If you want to post, subscribe first.
>  > -- Also note that this is not the r-help list where general R
> questions should go.
>  >
>
> --
> Patrick Burns
> patrick@burns-stat.com <mailto:patrick@burns-stat.com>
> http://www.burns-stat.com
> http://www.portfolioprobe.com/blog
> twitter: @portfolioprobe
>
> _______________________________________________
> R-SIG-Finance@r-project.org <mailto:R-SIG-Finance@r-project.org> mailing
> list
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> -- Subscriber-posting only. If you want to post, subscribe first.
> -- Also note that this is not the r-help list where general R questions
> should go.
>
>

-- 
Patrick Burns
patrick@burns-stat.com
http://www.burns-stat.com
http://www.portfolioprobe.com/blog
twitter: @portfolioprobe
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