On Mon, 11 Oct 2004, Heng Sun wrote:
> From the help document on KalmanLike, KalmanRun, etc.,
> I see the linear Gaussian state space model is
>
> a <- T a + R e
> y = Z' a + eta
>
> following the book of Durbin and Koopman.
>
> In practice, it is useful to run Kalman
> filtering/smoothing/forecasting with exogenous factor:
>
> a <- T a + L b + R e
> y = Z' a + M b + eta
>
> where b is some known vector (a function of time).
>
> Some other software like S-plus and Mathematica
> include the above exogenous factor. SsfPack by
> Koopman, etal. also has the factor built in the model
> to accommodate practical uses.
>
> So what is the rationale for R to leave off the
> exogenous factor? Is there a feasible way to convert
> the general model to the simple model in R?
What is the rationale for your raising this?
KalmanLike, KalmanRun, etc were written for R 1.5.0 as part of the ts
package (see my article in R-news), and the ts applications (see the See
Also section) do not need a so-called `exogenous factor' (which is not a
`factor'). R does not pretend to have facilities for whatever subject
area you mean (but do not say) by `in practice'. That's what addon
packages are for (and some do touch on this area).
We have no idea who `mathtester at yahoo.com' is: it is courteous to use a
signature and give your affiliation.
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595