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A Bayesian Analysis of Unobserved Component Models using Ox

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  • Charles S. Bos

    (VU University Amsterdam)

Abstract

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility models. While variants of the GARCH model are applied in scores of articles, use of the stochastic volatility model is less widespread. In this articleit is argued that one reason for this difference is the relative difficulty of estimating the unobserved stochastic volatility, and the varying approaches that have been taken for such estimation.In order to simplify the comprehension of these estimation methods, the main methods for estimating stochastic volatility are discussed, with focus on their commonalities. In this manner, the advantages of each method are investigated, resulting in a comparisonof the methods for their efficiency, difficulty-of-implementation, and precision.

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File URL: http://papers.tinbergen.nl/11048.pdf
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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-048/4.

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Date of creation: 03 Mar 2011
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Handle: RePEc:dgr:uvatin:20110048

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Web page: http://www.tinbergen.nl

Related research

Keywords: Stochastic volatility; estimation; methodology;

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References

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  1. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139.
  2. Jacques J. F. Commandeur & Siem Jan Koopman & Marius Ooms, . "Statistical Software for State Space Methods," Journal of Statistical Software, American Statistical Association, vol. 41(i01).
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Cited by:
  1. Nima Nonejad, 2013. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," CREATES Research Papers 2013-27, School of Economics and Management, University of Aarhus.

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