Journal article
On Bayesian analysis of nonlinear continuous-time autoregression models
Journal of time series analysis, Vol.28(5), pp.744-762
First Version received May 2007
09/2007
DOI: 10.1111/j.1467-9892.2007.00549.x
Abstract
This article introduces a method for performing fully Bayesian inference for nonlinear conditional autoregressive continuous-time models, based on a finite skeleton of observations. Our approach uses Markov chain Monte Carlo and involves imputing data from times at which observations are not made. It uses a reparameterization technique for the missing data, and because of the non-Markovian nature of the models, it is necessary to adopt an overlapping blocks scheme for sequentially updating segments of missing data. We illustrate the methodology using both simulated data and a data set from the S & P 500 index. © 2007 Blackwell Publishing Ltd.
Details
- Title: Subtitle
- On Bayesian analysis of nonlinear continuous-time autoregression models
- Creators
- O Stramer - University of IowaG. O Roberts - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of time series analysis, Vol.28(5), pp.744-762
- Edition
- First Version received May 2007
- Publisher
- Blackwell Publishing Ltd
- DOI
- 10.1111/j.1467-9892.2007.00549.x
- ISSN
- 0143-9782
- eISSN
- 1467-9892
- Number of pages
- 19
- Language
- English
- Date published
- 09/2007
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257631102771
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