[R] Spectral Analysis of Time Series in R
David Stoffer
dsstoffer at gmail.com
Sat Dec 6 05:14:25 CET 2008
You can do (1) and (2) [with some additional coding] using mvspec.R, which
you can download from http://www.stat.pitt.edu/stoffer/tsa2/chap7.htm ...
scroll down to the Spectral Envelope section and you'll find it there. You
can look at the top part of the examples to get an idea of how to use
mvspec.R ... once you have the spectral matrix estimate, you can code up
the extraction of the partial coherence and so on.
Alexander Schnebel wrote:
>
> Dear R Community,
>
> I am currently student at the Vienna University of Technology writing my
> Diploma thesis on causality in time series and doing some analyses of
> time series in R. I have the following questions:
>
> (1) Is there a function in R to estimate the PARTIAL spectral coherence
> of a multivariate time series? If yes, how does this work? Is there an
> test in R if the partial spectral coherence between two variables is
> zero? The functions I know (spectrum, etc.) only work to estimate the
> spectral coherence.
>
> (2) For some causality analysis I need an estimate of the inverse of the
> spectral density matrix of a multivariate time series. Is there any
> possibility in R to get this? Actually, I would be happy if I could at
> least get a functional estimate of the spectral density matrix. I guess
> this should work because R can plot the kernel density estimator of the
> spectral density, so it should be possible to extract the underlying
> function estimate.
>
> (3) Is there any possibility to do Granger Causality in R? That means
> fitting an VAR model and testing if some coefficients are zero.
>
> Thank you very much in advance!
>
> Best Regards,
> Alexander
> T
>
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>
-----
The power of accurate observation is commonly called cynicism
by those who have not got it. George Bernard Shaw
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