Thanks for your help!

I understand the general forecast; but I don't know how to use the info
that I only have to forecast the partial vector (the X's )... I already
have the Y and Z's...

i.e. there are some segments of X's that are missing, while the Y and Z's
are always available...


[x_i, y_i, z_i] for i=1...N are available for training;
[y_i, z_i] for i=N+1...2N are available while [x_i] for i=N+1...2N are
missing;
so on and so forth...
(N is the length of half-cycle and 2N is the length of the whole cycle;
i.e. for x_i the availability pattern is "A", "NA", "A", "NA" ... )

The goals are
1) to forecast [x_k, y_k, z_k] for k=2N+1
2) to estimate the missing [x_k] for k=N+1, ... 2N

After reading the package documentation and doing lots of Googling... I am
not very sure about how to achieve these using VECM ...

Anybody could please kindly give me some pointers?

Thanks a lot!


On Wed, Dec 14, 2011 at 2:56 PM, Matthieu Stigler <
matthieu.stigler@gmail.com> wrote:

> You will have generally more predictive power if you base your forecast on
> the VECM rather than just the long-run relationship.
>
> To do so, have a look at the package vars(), which allows you to estimate
> a VECM(), and then to predict based onit!
>
> Matthieu
>
>
>
> 2011/12/14 Mark Leeds <markleeds2@gmail.com>
>
>> 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!
>> >
>> >        [[alternative HTML version deleted]]
>> >
>> > _______________________________________________
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>> > https://stat.ethz.ch/mailman/listinfo/r-sig-finance
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>> > -- Also note that this is not the r-help list where general R questions
>> > should go.
>> >
>>
>>        [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-SIG-Finance@r-project.org mailing list
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>>
>
>

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