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A Bayesian Analysis of Unobserved Component Models using Ox

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  • Charles S. Bos

    (VU University Amsterdam)

Abstract

This discussion paper led to a publication in the 'Journal of Statistical Software' , 41(13), 1-24. 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.

Suggested Citation

  • Charles S. Bos, 2011. "A Bayesian Analysis of Unobserved Component Models using Ox," Tinbergen Institute Discussion Papers 11-048/4, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20110048
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    References listed on IDEAS

    as
    1. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139.
    2. Commandeur, Jacques J. F. & Koopman, Siem Jan & Ooms, Marius, 2011. "Statistical Software for State Space Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i01).
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    Cited by:

    1. Nonejad, Nima, 2014. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," MPRA Paper 55662, University Library of Munich, Germany.
    2. Yasutomo Murasawa, 2016. "The Beveridge–Nelson decomposition of mixed-frequency series," Empirical Economics, Springer, vol. 51(4), pages 1415-1441, December.
    3. Nima Nonejad, 2013. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," CREATES Research Papers 2013-27, Department of Economics and Business Economics, Aarhus University.
    4. repec:jss:jstsof:41:i01 is not listed on IDEAS
    5. Nonejad Nima, 2016. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," Journal of Time Series Econometrics, De Gruyter, vol. 8(1), pages 55-90, January.

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    More about this item

    Keywords

    Stochastic volatility; estimation; methodology;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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