hi: that's the equation that represents the stationarity relatonship. but
that equation implies an error correcting
relationship between the 3 variables. I've only dealt with the bivariate so
my guess would be to view
it as bivariate ( X and ( Y and Z))  and write the bivariate ecm out and
that will allow you to forecast ( assuming you have the values of the y_t-1
and z_t-1 at each t ). Eric's S+Finmetrics book has the details for the
bivariate case. hopefully he'll comment on whether converting  the
trivariate case to a bivariate case is legal econometrically. he also has
the general case ( Johansen ) in there but I haven't studied it. His book
is highly recommended for a nice intro. ( then enders, then hamilton or
hayashi )


mark





On Wed, Dec 14, 2011 at 10:32 AM, Michael <comtech.usa@gmail.com> wrote:

> Using cointegration coefficients to forecast?
>
> Hi all,
>
> I am looking for the following functionality in R... I couldn't find it.
> Please help me.
>
> Let's say the three time series X, Y, Z are cointegrated...
>
> We can find cointegration coefficients to be [1, -a, -b] (the 1 here is for
> normalization.)
>
> I then want to use X=a*Y+b*Z for forecasting X based on Y and Z values (or,
> filling out the missing values using the available values of Y and Z)
>
> i.e. using cointegration much like linear regression...
>
> How to do this in R
>
> Any thoughts?
>
> Thanks a lot!
>
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