[R-SIG-Finance] how to smooth timeseries without the lagging?

Mon Jul 27 11:59:24 CEST 2009

Sorry guys!

This is not a well posed question.
If you intend to "smooth" a time series all methods mentioned (and that I'm
aware of anyway) use some kind of TIME averaging - the best simple method
is probably kernel smoothing (nadaraya-watson smoother). Lowess (available
in R) locally weighted regression is another alternative.
One way or another you'll be doing some kind of "time averaging" - afterall
the smoothed solution is generally a projection of x_t onto the subspace
spanned by the history (e.g. x_(t-1) etc).

As I see it the only way to avoid "time averaging" is to use regression of
y_t on x1, x2, .. - this once again is the projection of y_t onto the
subspace spanned by x1, x2 etc. - but this is still an "average"!
Here of course you need independent predictors and once again these may
have time effects that are correlated with y_t - e.g. y_t may be correlated
with x_1(t-3), x_1 lagged 3 periods. In this situation you'll need dynamic
regression models.
But, nevertheless using regression is the most straightforward option to
avoid time averaging.

Gerard

             Eric Zivot                                                    
             <ezivot at u.washing                                             
             ton.edu>                                                   To 
             Sent by:                  michael.sankowski at gmail.com         
             r-sig-finance-bou                                          cc 
             nces at stat.math.et         "r-sig-finance at stat.math.ethz.ch"   
             hz.ch                     <r-sig-finance at stat.math.ethz.ch>,  
                                       r-sig-finance-bounces at stat.math.eth 
                                       z.ch                                
             25/07/2009 16:31                                      Subject 
                                       Re: [R-SIG-Finance] how to smooth   
                                       timeseries without the lagging?     

"smoothing" is not a well defined term. In the Kalman filter literature,
smoothing refers to state extraction at time t using all available
information (before and after t) and typically results in two-sided moving
average type algorithms. The whole point of smoothing is to reduce the
noise in the data and extract a "smooth" component. This invariably
involves some kind of averaging of the observation both before and after
the observation. So it doesn't make sense to have a "smoother" that does
not involve some lagging effect. Otherwise, you couldn't smooth. See

Harvey, A. C. and Koopman, S. J. (2000). Signal extraction and the
formulation of unobserved components
models. Econometrics Journal, Vol. 3, pp. 84-107.

for a nice discussion.

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*  Eric Zivot
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*  Department of Economics                                     *
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On Sat, 25 Jul 2009 michael.sankowski at gmail.com wrote:

> Also try MESA. I was suppposed to do this for josh but have been on other
projects.
> Sent via BlackBerry from T-Mobile
>
> -----Original Message-----
> From: Sean Carmody <seancarmody at gmail.com>
>
> Date: Sat, 25 Jul 2009 19:56:59
> To: Josuah Rechtsteiner<rechtsteiner at bgki.net>
> Cc: <r-sig-finance at stat.math.ethz.ch>
> Subject: Re: [R-SIG-Finance] how to smooth timeseries without the
lagging?
>
>
> Or a Henderson filter. If you have a filter that looks forward as well as
> back, it will not have the lag effect. Then it'll need special treatment
at
> the end of the series.
>
> (Sorry for the double email Josuah).
>
> Sean.
>
> On Sat, Jul 25, 2009 at 7:12 PM, Josuah Rechtsteiner
> <rechtsteiner at bgki.net>wrote:
>
>> maybe kalman filter is what you are looking for.
>>
>>
>>
>> Am 25.07.2009 um 05:44 schrieb Michael:
>>
>>
>>  Hi all,
>>>
>>> If I use a moving average, it will smooth the choppy time series, but
>>> it will lead to lagging...
>>>
>>> How do I smooth timeseries without the lagging effect?
>>>
>>> Thanks!
>>>
>>> _______________________________________________
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>>
>> _______________________________________________
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>
>
>
> --
> Sean Carmody
>
> The Stubborn Mule
> http://www.stubbornmule.net
> http://twitter.com/seancarmody
>
>            [[alternative HTML version deleted]]
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