The simulate function in dse lets you specify the model and the
distribution of the noise term (or even their values so you can get any
distribution you like). So, you should be able to do what you want,
with either a VAR(p) or a vector ARMA process. If you are getting a
process that explodes then your model is probably not stable. If it is a
dse TSmodel you can check it with stability(), see ?stability in dse.
Beware that the condition Modulus <1 depends on whether your lagged
parameters are specified on the left or right side of the equation. This
changes the sign of the lag parameters and inverts the condition. Dse
assumes lagged terms are specified on the left side, which is a bit
unusual compared to introductory text books. However, when you get to
hard problems it has advantages because the AR term is a matrix
polynomial ring and so it is easier to apply some useful mathematics.
Paul
Date: Wed, 4 Jan 2012 05:17:05 -0800 (PST)
From: statquant2<statqu...@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 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 processes...
When I try to generate random VAR(p) processes they seems to explode
(clearly they are not weakly stationary...)
Is there a way somebody know?
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