Journal article
Likelihood inference for discretely observed non-linear diffusions.
- Abstract:
- This paper is concerned with the Bayesian estimation of nonlinear stochastic differential equations when observations are discretely sampled. The estimation framework relies on the introduction of latent auxiliary data to complete the missing diffusion between each pair of measurements. Tuned Markov chain Monte Carlo (MCMC) methods based on the Metropolis-Hastings algorithm, in conjunction with the Euler-Maruyama discretization scheme, are used to sample the posterior distribution of the latent data and the model parameters. Techniques for computing the likelihood function, the marginal likelihood, and diagnostic measures (all based on the MCMC output) are developed. Examples using simulated and real data are presented and discussed in detail.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 808.5KB, Terms of use)
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- Publisher copy:
- 10.1111/1468-0262.00226
Authors
+ Economic and Social Research Council
More from this funder
- Funding agency for:
- Shephard, N
- Grant:
- R000238391
- Publisher:
- Econometric Society
- Journal:
- Econometrica More from this journal
- Volume:
- 69
- Issue:
- 4
- Pages:
- 959 - 993
- Publication date:
- 2001-01-01
- DOI:
- ISSN:
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0012-9682
- Language:
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English
- UUID:
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uuid:32366322-5b0e-4f38-b18b-89c11fd44265
- Local pid:
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oai:economics.ouls.ox.ac.uk:13896
- Deposit date:
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2011-08-16
Terms of use
- Copyright holder:
- Econometric Society
- Copyright date:
- 2001
- Notes:
- The copyright to this article is held by the Econometric Society, http://www.econometricsociety.org/. It may be downloaded, printed and reproduced only for personal or classroom use. Absolutely no downloading or copying may be done for, or on behalf of, any for-profit commercial firm or for other commercial purpose without the explicit permission of the Econometric Society. For this purpose, contact the Editorial Office of the Econometric Society at [email protected].
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