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)
Date: 1998
View citations in EconPapers

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

Related works:
Working Paper: Likelihood inference for discretely observed non-linear diffusions (2000)
Journal Article: Likelihood Inference for Discretely Observed Nonlinear Diffusions (2001)
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: http://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.
Series data maintained by Maxine Collett ().