I think that you must approach this in a different way.

1 Draw a set of random eigenvalues with modulus < 1
2 Draw a set of random eigenvalues vectors.
3 From these you can, with some matrix manipulations, derive the
corresponding Var coefficients.

If your original coefficients were drawn at random I suspect that the VAR
would not be stable. I am curious about what you are trying to do.

John

On Friday, 13 January 2012, statquant2 <statquant@gmail.com> wrote:
> Hello Paul
> Thanks for the answer but my point is not how to simulate a VAR(p) process
> and check that it is stable.
> My question is more how can I generate a VAR(p) such that I already know
> that it is stable.
>
> We know a condition that assure that it is stable (see first message) but
> this is not a condition on coefficients etc...
> What I want is
> generate say a 1000 random VAR(3) processes over say 500 time periods that
> will be STABLE (meaning If I run stability() all will pass the test)
>
> When I try to do that it seems that none of the VAR I am generating pass
> this test, so I assume that the class of stable VAR(p) is very small
> compared to the whole VAR(p) process.
>
>
>
> --
> View this message in context:
http://r.789695.n4.nabble.com/simulating-stable-VAR-process-tp4261177p4291835.html
> Sent from the R help mailing list archive at Nabble.com.
>
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> and provide commented, minimal, self-contained, reproducible code.
>

-- 
John C Frain
Economics Department
Trinity College Dublin
Dublin 2
Ireland
www.tcd.ie/Economics/staff/frainj/home.html
mailto:frainj@tcd.ie
mailto:frainj@gmail.com

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