Hi: bierens has a paper on modelling beer sales in the netherland ( I'm
pretty sure it's on the net.
if not, I have a copy somewhere I think )  using an ARIMAX. why don't you
take his paper and his data and see if you get the same estimates using R.
That's one way if you'll know if you're doing the steps correctly. I don't
have cryer and chan and I've never used the TSA package so I can't say
anything else.






On Thu, Feb 21, 2013 at 5:26 AM, Jose Iparraguirre <
Jose.Iparraguirre@ageuk.org.uk> wrote:

> Hi
>
> I'm trying to measure the effect of a policy intervention (Box and Tiao,
> 1975).
> This query has to do with the coding of the model rather than with the
> particulars of my dataset, so I'm not providing the actual dataset (or a
> simulated one) in this case, apart from some general description.
>
> The time series are of length n=34 (annual observations between 1977 and
> 2010). The policy measure was introduced in 2000 and it has been
> implemented once a year ever since.
>
> The variable of interest (VI) is continuous, and I have four continuous
> covariates (CO1-CO4), plus the dummy intervention variable (DUM) which is
> equal to 0 between 1977 and 1999 and equal to 1 since 2000.
>
> I thought of using an ARIMAX model, with the arimax() function in the TSA
> package to fit the transfer function. I'm interested in modelling the
> intervention effect as a step function.
>
> I specified the model thus:
>
>
> a.       I've checked the ARIMA properties of each series using the
> auto.arima() function (from the 'forecast' package) -the VI was found to
> best fit an ARIMA(0,1,1) model and the first covariate an ARIMA(1,0,0),
> whereas the other covariates were white noise.
>
>
>
> b.      To facilitate the specification of the various models  (the
> different model specifications dropped variables or added additional
> covariates, etc, without changing the general structure of the syntax
> below), I defined the following design matrix:
>
>
>
> > xreg.1 <-  model.matrix(~CO1+ CO2+ CO3+ CO4)[,2:5]
>
>
>
> c.       Following Cryer and Chan (2008, ch. 11, p. 255), I wrote models
> such as this:
>
>
>
> > arimax.1 <- arimax(VI, order=c(0,1,1),
>
> + xtransf=data.frame(xreg.1,dummy1=DUM),
>
> + transfer=list(c(1,0),c(0,0),c(0,0),c(0,0),c(0,0)))
>
>
> d.      So, I get estimates for the following coefficients:
>
>
> ma1;  CO1-AR1;  CO1-MA0;  CO2-MA0;  CO3-MA0;  CO4-MA0;  DUM-MA0
>
> Can you spot any problems with the general modelling approach or in this
> specification?
>
> Many thanks in advance,
>
> José
>
>
>
> José Iparraguirre
> Chief Economist
> Age UK
>
> T 020 303 31482
> E Jose.Iparraguirre@ageuk.org.uk<mailto:Jose.Iparraguirre@ageuk.org.uk>
> Twitter @jose.iparraguirre@ageuk
>
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