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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

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Publisher copy:
10.1111/1468-0262.00226

Authors


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Institution:
University of Oxford
Oxford college:
Nuffield College
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Institution:
John M. Olin School of Business
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Institution:
University of Oxford
Oxford college:
Nuffield College
Department:
Social Sciences Division - Economics
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Funding agency for:
Ola Elerian
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Funding agency for:
Neil Shephard
Journal:
Econometrica Journal website
Volume:
69
Issue:
4
Pages:
959-993
Publication date:
2001-07-05
DOI:
ISSN:
0012-9682
URN:
uuid:199e8d19-310c-45c6-98b7-bca82c776744
Local pid:
ora:2258

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