Mark, statquant2
As I understand the question it is not to test if a VAR is stable but how
to construct a VAR that is stable and automatically satisfies the condition
Mark has taken from Lutkohl. The algorithm that I have set out will
automatically satisfy that condition.The matrix that should be
Mark
This should be reasonably straightforward. In the simplest case you wih to
draw a random complex number in the unit circle. This is best done in polar
coordinates.
If r is a random mumber on (0,1) and theta a random number on (0, 2 Pi)
then if x=r cos(theta) and y= r sin(theta), x + i y is
gotcha john. thanks.
On Sat, Jan 14, 2012 at 9:28 PM, John C Frain fra...@gmail.com wrote:
Mark
This should be reasonably straightforward. In the simplest case you wih to
draw a random complex number in the unit circle. This is best done in polar
coordinates.
If r is a random mumber on
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
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
useful mathematics.
Paul
Date: Wed, 4 Jan 2012 05:17:05 -0800 (PST)
From: statquant2statqu...@gmail.com
To:r-help@r-project.org
Subject: Re: [R] simulating stable VAR process
Message-ID:1325683025141-4261210.p...@n4.nabble.com
Content-Type: text/plain; charset=us-ascii
More specifically.
I know
Hello all,
I looking at package dse or vars or mAr
I know how to simulate a VAR(p) process, my problem is that most of those
processes are unstable (not weakly stationary).
Do anybody know how to generate a random VAR (or VARMA even better) process
that is weakly stationary?
Thanks
--
View this
More specifically.
I know that a condition for a VAR(p) process to be stable (weakly
stationary) is that the companion form of the equation (see AWESOME Pfaff
book analysis of integrated and cointegrated time series in R) as
eigenvalues of modulus 1.
My problem is that I want to generate such
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