Likelihood inference for Discretely Observed Non-linear Diffusions.

Working paper

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.

Authors: Elerian, O; Chib, S; Shephard, N

• Publisher: Nuffield College (University of Oxford)
• Series: Economics Working Papers
• Publication date: 1998-01-01
• Language: English