On Bayesian analysis of nonlinear continuous-time autoregression models
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. Copyright 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd.
Year of publication: |
2007
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Authors: | Stramer, O. ; Roberts, G. O. |
Published in: |
Journal of Time Series Analysis. - Wiley Blackwell, ISSN 0143-9782. - Vol. 28.2007, 5, p. 744-762
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Publisher: |
Wiley Blackwell |
Saved in:
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