[R] Structural TS and recursive estimation

[Pfaff, Bernhard]

Hello Julien,

as I understand you correctly, you want to perform pseudo-ex ante forecasts.
In this case, you can place the data extraction (i.e. length of the sample
period) and the program code for estimation into a for-loop for pseudo ex
ante forecasting.
Therefore estimating your model recursively over a time span in the past and
writing each time the n-step ahead forecasts into another object. The latest
estimation is then carried out until: today's period - forecast span.

Bernhard 

-----Original Message-----
From: ripley at stats.ox.ac.uk [mailto:ripley at stats.ox.ac.uk]
Sent: 05 August 2002 11:36
To: julien.ruiz at airfrance.fr
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] Structural TS and recursive estimation


On Mon, 5 Aug 2002 julien.ruiz at airfrance.fr wrote:

> Since my question is quite theorical, I am not sure whether it is the
right
> place to ask, but anyway...
> I am working on time series and I looked at some way to fit my data
through
> arima models.
> Since these data are updated frequently, I was looking at a way to update
> the model "on line" (to get a kind of recursive estimation)
> So the next step was to express the arima models as state-space
> (structural) models.
> The idea was to use the recursive formulaes of a Kalman Filter, in order
to
> get an estimation of the kind of the recursive least square.
> But it seems to me that the estimation of these structural models requires
> a likelihood maximization which is not recursive.
>
> So my question is :
> In a structural model, can the likelihood maximization be done recursively
> ?
>
> Upon what I read in 2 first articles of the 2/2 issue of R News, I don't
> think it is done this way in R.

1) ARIMA fitting *is* done via state-space models, but structural models
are something different.

2) You can't (in general) do ML estimation of the parameters of a
state-space model recursively.  Nor is that what recursive least squares
estimates.

For more details, see the references in the article you mention,
especially the Durbin & Koopman book.

-- 
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 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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