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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 late...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1111/1468-0262.00226

Authors


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Institution:
University of Oxford
Siddhartha Chib More by this author
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Institution:
University of Oxford
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Funding agency for:
Ola Elerian
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Funding agency for:
Neil Shephard
Publisher:
Econometric Society Publisher's website
Journal:
Econometrica Journal website
Volume:
69
Issue:
4
Publication date:
2001
DOI:
URN:
uuid:32366322-5b0e-4f38-b18b-89c11fd44265
Local pid:
oai:economics.ouls.ox.ac.uk:13896
Language:
English

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