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A Bayesian Analysis of Unobserved Component Models Using Ox

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  • Charles S. Bos

Abstract

This article details a Bayesian analysis of the Nile river flow data, using a similar state space model as other articles in this volume. For this data set, Metropolis-Hastings and Gibbs sampling algorithms are implemented in the programming language Ox. These Markov chain Monte Carlo methods only provide output conditioned upon the full data set. For filtered output, conditioning only on past observations, the particle filter is introduced. The sampling methods are flexible, and this advantage is used to extend the model to incorporate a stochastic volatility process. The volatility changes both in the Nile data and also in daily S&P 500 return data are investigated. The posterior density of parameters and states is found to provide information on which elements of the model are easily identifiable, and which elements are estimated with less precision.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Statistical Software.

Volume (Year): 41 ()
Issue (Month): i13 ()
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Handle: RePEc:jss:jstsof:41:i13

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  1. 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).
  2. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139.
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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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