R Time Series Issue: Seasonal ARIMA Models

In my previous post, I mentioned that there are seasonal non-seasonal (S-NS) multiplicative models and seasonal non-seasonal additive models.

About these two types of models, Pankratz (1983) tells us that:

Multiplicative models are an excellent starting place for modeling S-NS realizations, and they often give good results. Most computer programs for estimating UBJ-ARIMA models provide at least an option for multiplicative seasonality; some simply assume that seasonal models are multiplicative.

It is then said that:

Occasionally, the assumption that the seasonal and nonseasonal elements are multiplicative is inappropriate.

This led me to question whether or not S-NS models in R using the arima() function are multiplicative by assumption or if there is an option to choose between multiplicative and additive models.

To fit a S-NS model in R, we can specify the function using code in the following form:

arima(x, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = s)

Other options can be specified, but there doesn't appear to be any reference to say whether or not the models are multiplicative by assumption.

I'm not the first to raise this question about S-NS models in R. The question was asked on the R mailing list here, but no answer was given. A follow up question was asked and in the email it is revealed that the arima() function assumes the models to be multiplicative.

The question now is: how to model S-NS additive models in R?

For the moment, one way around the problem of estimating S-NS additive models would be to use the lm() function. A proper solution would be to write code to slightly revamp arima(). I'm not sure if anyone is working on this problem, so I might try and write my own function that will do the trick.