You can either use the package vars and function cajorls() on a object
obtained with a previous ca.jo() call, or use package tsDyn and use the
function VECM().

To get forecasts, you can however only use the function cajorls() which
will give a simple regression object (reg.number=1) or a multiple
regression object mlm, for which a predict() function is available.



2011/12/14 Mark Leeds <markleeds2@gmail.com>

> thanks matthieu. my idea was el wrongo. is there a way (an R function ) to
> go from the estimated cointegrated vectors to the associated vecm. thanks.
>
>
> On Wed, Dec 14, 2011 at 3:56 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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>>> >
>>>
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>>>
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>>
>>
>

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