Hi: I'm not familar with Prof. Wildi's filters or the HP filter but the
standard kalman
filtering/smoothing algorithms are in dse, sspir and dlm. Well, definitely
dlm has
smoothing but I bet sspir and dse have them also.

I'm most familiar with the notation in  dlm so I've only used that
package. note that the kalman filtering notation can be extremely confusing
because the parameter symbols change from one KF setup to another  and even
the time subscripting notation can vary  depending on whether t is shifted
in the observation equation or not. but, if you're not used to one
notation, then any of them can probably help you to some degree. paul
didn't mention dse probably because he didn't want to self-promote !!!!!!
It's also appreciated if Prof Wildi wants to comment because maybe
his filters are very different from the ones in the packages I mentioned
and I could
be leading you astray.


mark


On Sat, Feb 25, 2012 at 12:09 PM, Michael <comtech.usa@gmail.com> wrote:

> Using you guys' term, I suspect the paper used "2-sided" filter, which
> "peeks" into the future, so the result in that paper is bogus...
>
> I want to find the "1-sided" version of HP filter and Kernel Smoother, and
> see if they work...
>
> Any thoughts and pointers?
>
> Thanks!
>
> On Sat, Feb 25, 2012 at 1:48 AM, Wildi Marc (wlmr) <wlmr@zhaw.ch> wrote:
>
> > I'm using the term as an `economist' and/or as an `engineer', hopefully
> > without adding confusion to the topic. Specifically, a real-time filter
> is
> > one-sided (which might sound redundant to some): it is also called
> > concurrent filter in time series analysis (where target signals are
> > considered to be outputs of bi-infinite symmetric filters: not
> > smoothers...). Real-time data means: data as it arrives (possibly being
> > revised in later vintages). A paper constructing real-time filters in the
> > case of real-time data (mixing both concepts) is proposed in
> >
> http://blog.zhaw.ch/idp/sefblog/index.php?/archives/205-7th-Annual-CIRANO-Workshop-on-Data-Revision-in-Macroeconomic-Forecasting-and-Policy.html
> >
> > Marc
> >
> > ________________________________________
> > Von: r-sig-finance-bounces@r-project.org [
> > r-sig-finance-bounces@r-project.org]&quot; im Auftrag von &quot;Paul
> > Gilbert [pgilbert902@gmail.com]
> > Gesendet: Samstag, 25. Februar 2012 01:04
> > Bis: r-sig-finance@r-project.org
> > Betreff: Re: [R-SIG-Finance] Causal version of HP filter and Kernel
> > Smoothing in R?
> >
> > Some pedantic points regarding correct terminology:
> >
> > On 12-02-24 06:00 PM, Brian G. Peterson wrote:
> > > As usual, it helps to use the correct terminology.
> > >
> > >
> > > The term usually employed is not 'causal' but 'one sided' or 'two
> sided'
> > > filters.
> >
> > Economists usually employ the terms 'one sided' and 'two sided'. In
> > engineering, physics, and mathematics, I think the terms 'filter' and
> > 'smoother' are still used. (But yes, 'causal' usually has to do with
> > something else.)
> >
> > > In classic state space models, the two sided filter is often
> > > called a 'smoother', and the one-sided version is called a 'filter'.
> > > See any introduction to Kalman filters for examples, since the Kalman
> > > may easily by one sided or two sided.
> >
> > Even in the classic case this is not specific to state-space models. The
> > term filter meant it could be used to filter incoming signals without
> > knowledge of the future, while a smoother needs future information. So,
> > a filter could be used to do realtime control, while a smoother could
> not.
> > >
> > > High pass filters are also quite trivial, as equation 4 in your
> > > reference demonstrates.
> > >
> > > I may be incorrect, having spent only a few moments on it, but I see
> > > nothing in this paper to indicate that the kernel smoothing in equation
> > > 6 is not equally trivial.
> > >
> > > Marc Wildi has written extensively on the topic of real time
> (one-sided)
> > > filters, and his R code is public.
> > >
> > Engineers use the term 'realtime data' to mean what I think most people
> > would understand as 'look at the data as it is arriving', which implies
> > using a (one-sided) filter.  Economists use the term 'realtime data' to
> > mean 'look at the data as it arrived'. That is, the vintages of the data
> > that were available at different points in time. Thus a realtime
> > analysis for an economist is a consideration of the revisions in the
> > data. I think Marc Wildi uses the term as an economist, not as an
> engineer.
> >
> > (I warned you this is pedantic.)
> > Paul
> >
> > > On Fri, 2012-02-24 at 16:47 -0600, Michael wrote:
> > >> Thanks Brian.
> > >>
> > >> http://xfi.exeter.ac.uk/workingpapers/0804.pdf
> > >>
> > >> My understanding is that those kernel smoothers and HP filters are all
> > >> non-causal...
> > >>
> > >> i.e. they peek into future from time-series point-of-view...
> > >>
> > >> Therefore, I am looking for the Causal version.
> > >>
> > >> Thank you!
> > >>
> > >>
> > >> On Fri, Feb 24, 2012 at 4:41 PM, Brian G. Peterson
> > >> <brian@braverock.com>  wrote:
> > >>
> > >>          On Fri, 2012-02-24 at 15:39 -0600, Michael wrote:
> > >>          >  Hi all,
> > >>          >
> > >>          >  I am reading a paper talking about extracting low
> frequency
> > >>          trend in FX
> > >>          >  markets and then devising trading strategies based on
> those
> > >>          low frequency
> > >>          >  trends.
> > >>          >
> > >>          >  I was wondering if there are Causal version of HP filter
> and
> > >>          kernel
> > >>          >  Smoothing functions in R, as mentioned in that paper?
> > >>          >
> > >>          >  I did quite some search but couldn't find any ... Could
> you
> > >>          please help me?
> > >>
> > >>
> > >>          It would be easier for people to decide whether to help you
> if
> > >>          you
> > >>          actually provided the reference to the paper you are looking
> > >>          to
> > >>          replicate.
> > >>
> > >>          There are many kernel smoothing methods in various R
> packages,
> > >>          which
> > >>          your 'quite some search' I am sure uncovered, *and* kernel
> > >>          smoothing
> > >>          mechanisms are typically rather trivial to code.  So without
> > >>          the
> > >>          reference it is hard to even begin to evaluate which of them
> > >>          might do
> > >>          what you are looking for.  Also, it would be polite for you
> to
> > >>          indicate
> > >>          in what way the kernel smoothing mechanisms provided by
> > >>          specific
> > >>          packages do not match the methodology you desire.
> > >>
> > >>          --
> > >>          Brian G. Peterson
> > >>          http://braverock.com/brian/
> > >>          Ph: 773-459-4973
> > >>          IM: bgpbraverock
> > >>
> > >>
> > >
> >
> > _______________________________________________
> > R-SIG-Finance@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-finance
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> > -- Also note that this is not the r-help list where general R questions
> > should go.
> > _______________________________________________
> > R-SIG-Finance@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-finance
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> > -- Also note that this is not the r-help list where general R questions
> > should go.
> >
>
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