Likelihood INference for Discretely Observed Non-linear Diffusions

Ola Elerian (), S. Chib and Neil Shephard ()

Economics Papers from Economics Group, Nuffield College, University of Oxford

Abstract: This paper is concerned with the Bayesian estimation of non-linear stochastic differential equations when only discrete observations are available. The estimation is carried out using a tuned MCMC method, in particular a blcked Metropolis-Hastings algorithm, by introducing auxiliary points and by using the Euler-Maruyama discretisation scheme. Techniques for computing the likelihood function, the marginal likelihood and diagnostic measures (all based on the MCMC output) are presented.

Keywords: MAXIMUM LIKELIHOOD; SIMULATION; EVALUATION (search for similar items in EconPapers)
JEL-codes: C13 C15 (search for similar items in EconPapers)
Pages: 43 pages
Date: 1998
References: Add references at CitEc
Citations: View citations in EconPapers (127)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Likelihood Inference for Discretely Observed Nonlinear Diffusions (2001)
Working Paper: Likelihood inference for discretely observed non-linear diffusions (2000)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nuf:econwp:146

Access Statistics for this paper

More papers in Economics Papers from Economics Group, Nuffield College, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by Maxine Collett ().