Arimax with intervention dummy and multiple covariates

Thank you very much my friend.
El 21/02/2013 11:21, "Mark Leeds" <markleeds2 at gmail.com> escribi?:

let me look. I have hardcopy so hopefully I have computer copy. get 
back in a few minutes.


On Thu, Feb 21, 2013 at 11:06 AM, Paul Bernal <paulbernal07 at gmail.com>wrote:

Could you please provide me with a copy of that text?

Best regards,

Paul
El 21/02/2013 10:59, "Mark Leeds" <markleeds2 at gmail.com> escribi?:

 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 at 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,

Jos?



Jos? Iparraguirre
Chief Economist
Age UK

T 020 303 31482
E Jose.Iparraguirre at ageuk.org.uk<mailto:

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