[R] Transfer Function Modeling

[Richard A. Bilonick]

I think I've figured out the use of "filter" in the ts package, at least 
for a simple AR model. I simulated a simple AR time series, modeled it 
using "arima",  and then used "filter" to compute the 1-step ahead 
forecasts:

> y.arma <- arima.sim(list(ar=0.8),n=200)
> y.arma.arima <- arima(y.arma,order=c(1,0,0),include.mean=FALSE)
> y.arma.arima
Call:
arima(x = y.arma, order = c(1, 0, 0), include.mean = FALSE)
Coefficients:
        ar1
     0.7594
s.e.  0.0455
sigma^2 estimated as 1.059:  log likelihood = -290,  aic = 583.99

> plot(y.arma[-1],lines="l")
> lines(filter(y.arma,y.arma.arima$coef,method="con")[-200],lty=2)
So using the "convolution" method appears to produce the correct 
forecast for the y.arma series.

But suppose I had generated and fitted an ARMA(1,1) or ARIMA(1,1,1) 
model? How would I use "filter" in this case?

Rick B.

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