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
Spectral Analysis of Time Series in R
3 messages · Alexander Schnebel, Pfaff, Bernhard Dr., stoffer@pitt.edu
Hello Alexander, for (3) see the CRAN-package "vars". Best, Bernhard
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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2 days later
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
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
----- The power of accurate observation is commonly called cynicism by those who have not got it. George Bernard Shaw
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