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 at 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