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 late...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Publisher copy:
- 10.1111/1468-0262.00226
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Authors
Funding
+ Economic and Social Research Council
More from this funder
Funding agency for:
Shephard, N
Grant:
R000235488; R000238391
+ European Union
More from this funder
Funding agency for:
Shephard, N
Grant:
R000235488; R000238391
Bibliographic Details
- Journal:
- Econometrica Journal website
- Volume:
- 69
- Issue:
- 4
- Pages:
- 959-993
- Publication date:
- 2001-07-01
- DOI:
- ISSN:
-
0012-9682
Item Description
- Language:
- English
- Keywords:
- Subjects:
- UUID:
-
uuid:199e8d19-310c-45c6-98b7-bca82c776744
- Local pid:
- ora:2258
- Deposit date:
- 2008-08-12
Related Items
Terms of use
- Copyright holder:
- The Econometric Society
- Copyright date:
- 2001
- Notes:
- The full-text of this article is not currently available in ORA. Citation: Elerian, O., Chib, S. & Shephard, N. (2001). 'Likelihood inference for discretely observed nonlinear diffusions', Econometrica, 69(4), 959-993. [Available at http://www3.interscience.wiley.com/journal/118482539/home].
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