Forgot to say thanks for the suggestions, guys. I see my original message
got pretty jumbled, notation wise (I incorrectly figured words symbols
counted as plain text), but your ideas put me on the right track. I am now
well under way to creating the models I want, thanks to you.

-Hubert

On Sat, Dec 5, 2009 at 9:17 PM, <markleeds@verizon.net> wrote:

> giovanni's dlm book is also very nice for learning the dlm package. the dlm
> notation is along the notation of west and harrison . the package to use can
> often be linked to where one learned about the kalman filter so that can
> sometimes guide your package decison.
>
>
>
>
>
>
> On Dec 5, 2009, *Gabor Grothendieck* <ggrothendieck@gmail.com> wrote:
>
> If you just need R examples then there is a paper on the jstatsoft site
> that
> has R examples of Kalman filtering with sspir. Try google.
>
> On Sat, Dec 5, 2009 at 8:58 AM, Hubert Colt <hubert.colt@gmail.com> wrote:
>
> > Hi,
> >
> > My apologies in advance if my question seems somewhat basic. I am for the
> > first time in need of using a Kalman filter in R, but am unsure as to
> which
> > of the available packages I should choose. There seems to be a lot of
> > different ones to chose from, from the regular KalmanLike and -Run from
> the
> > base package, to the Kalman filter in the dlm package, the filters from
> ***
> > ssall.R*<
> >
> https://owa.nhh.no/exchweb/bin/redir.asp?URL=http://www.stat.pitt.edu/stoffer/tsa2/Rcode/New6/ssall.R
> > >
> > . used by Schumway and Stoffer and the KFAS package by Jouni Lehtonen
> (***
> > https://stat.ethz.ch/pipermail/r-packages/2009/001049.html*<
> >
> https://owa.nhh.no/exchweb/bin/redir.asp?URL=https://stat.ethz.ch/pipermail/r-packages/2009/001049.html
> > >
> > ).
> >
> > The time series I wish to use the filter on are time invariant, with
> > observations every month, and have no missing data. I am trying to judge
> > whether one portfolio of stocks outperforms another, and hope to utilize
> > the
> > Kalman Filter in order to reduce the noise in the sample.
> >
> > I have reason to believe the equation explaining returns looks like this:
> >
> >
> > Yt = Ä¯t + Ä 1t * MRKTPREMt + Ä 2t * SMBt + Ä 3t * HMLt + Ä 4t * MOMt
>
> >
> > Where Yt is the excess return of portfolio 1 (portfolio1-portfolio2) in
> > month t, SMB and HML are the Fama French Factors, MOM is momentum and
> > MRKTPREM is the market premium. I have time series detailing all of
> these,
> > but have reason to believe that the loadings and alpha vary over time.
> >
> > First and foremost I would like to obtain a maximum likelihood row of
> time
> > variable alphas, with the other loadings fixed, in order to surmise if
> they
> > are jointly positive. However, If the model allows it, I would like to
> > introduce the possibility of non-fixed betas as well.
> >
> > I have ordered Schumway and Stoffer's book, but due to the time
> constraints
> > of
> > my research I would be very thankful if one of you could help point me in
> > the right direction. If anyone knows any easy to use non-R software that
> > could do the operation, I would be glad to hear from you too.
> >
> > I am using R 2.10 and my OS is Windows Vista, in case that has any
> impact.
> >
> > Thank you in advance for taking time out of your day to answer me.
> >
> > -Hubert
> >
> > [[alternative HTML version deleted]]
> >
> >
> > _______________________________________________
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