[R] estimating an ARIMA model with constraints

[Laurent Duvernet]

Hi,

I am trying to estimate an ARIMA model in the case where I have some 
specific knowledge about the coefficients that should be included in the 
model. Take a classical ARIMA (or even ARMA) model:

P(B) X(t) = Q(B) epsilon(t),

where X(t) is the data, epsilon is a white noise, B is the backward operator 
and P and Q are some polynoms. Additionally, assume that you know in advance 
how P and Q look like. Typically, P could be something like this:

P(x) = (1 - a(1)*x - a(2)*x^2) * (1 - b(1)*x^23 - b(2)*x^24) * (1 - 
c(1)*x^168)

(That is in the case of hourly data, with lags 23 and 24 corresponding to 
the day, and lag 168 for the week.) How do you estimate this kind of model 
with R? The arima() and arima0() functions in the stats package do not allow 
this kind of constraints on the polynoms. I've searched in the packages 
dedicated to time series analysis, but I have not found a solution. Has 
anyone an idea?

Thanks in advance!

Laurent Duvernet
EDF R&D