Bayesian non-parametric signal extraction for Gaussian time series
We consider the problem of unobserved components in time series from a Bayesian non-parametric perspective. The identification conditions are treated as unknown and analyzed in a probabilistic framework. In particular, informative prior distributions force the spectral decomposition to be in an identifiable region. Then, the likelihood function adapts the prior decompositions to the data. A full Bayesian analysis of unobserved components will be presented for financial high frequency data. Particularly, a three component model (long-term, intra-daily and short-term) will be analyzed to emphasize the importance and the potential of this work when dealing with the Value-at-Risk analysis. A second astronomical application will show how to deal with multiple periodicities.
Year of publication: |
2010
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Authors: | Macaro, Christian |
Published in: |
Journal of Econometrics. - Elsevier, ISSN 0304-4076. - Vol. 157.2010, 2, p. 381-395
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Publisher: |
Elsevier |
Keywords: | Unobserved components Spectral representation Identification conditions |
Saved in:
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