A Bayesian Analysis of Unobserved Component Models Using Ox
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 p...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2011-05-01
|
| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | http://www.jstatsoft.org/v41/i13/paper |
| _version_ | 1818199496996159488 |
|---|---|
| author | Charles S. Bos |
| author_facet | Charles S. Bos |
| author_sort | Charles S. Bos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T02:22:42Z |
| format | Article |
| id | doaj.art-7f0f8d7f3b3c4cb3abb0d71b269920f5 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-12T02:22:42Z |
| publishDate | 2011-05-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-7f0f8d7f3b3c4cb3abb0d71b269920f52022-12-22T00:41:38ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602011-05-014113A Bayesian Analysis of Unobserved Component Models Using OxCharles S. BosThis 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.http://www.jstatsoft.org/v41/i13/paperstate space methodsunobserved componentsBayesstochastic volatility |
| spellingShingle | Charles S. Bos A Bayesian Analysis of Unobserved Component Models Using Ox Journal of Statistical Software state space methods unobserved components Bayes stochastic volatility |
| title | A Bayesian Analysis of Unobserved Component Models Using Ox |
| title_full | A Bayesian Analysis of Unobserved Component Models Using Ox |
| title_fullStr | A Bayesian Analysis of Unobserved Component Models Using Ox |
| title_full_unstemmed | A Bayesian Analysis of Unobserved Component Models Using Ox |
| title_short | A Bayesian Analysis of Unobserved Component Models Using Ox |
| title_sort | bayesian analysis of unobserved component models using ox |
| topic | state space methods unobserved components Bayes stochastic volatility |
| url | http://www.jstatsoft.org/v41/i13/paper |
| work_keys_str_mv | AT charlessbos abayesiananalysisofunobservedcomponentmodelsusingox AT charlessbos bayesiananalysisofunobservedcomponentmodelsusingox |