期刊
JOURNAL OF TIME SERIES ANALYSIS
卷 28, 期 5, 页码 744-762出版社
WILEY
DOI: 10.1111/j.1467-9892.2007.00549.x
关键词
continuous-time autoregression; Markov chain Monte Carlo; non-linear 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.
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