- 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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Publisher copy:
- 10.1111/1468-0262.00226
-
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- 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
- 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].
Journal article
Likelihood inference for discretely observed non-linear diffusions
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