# Working paper

## Likelihood inference for Discretely Observed Non-linear Diffusions.

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.

### Authors

Publisher:
Nuffield College (University of Oxford)
Series:
Economics Working Papers
Publication date:
1998-01-01
Language:
English
UUID:
uuid:3d251ac3-8050-4ca8-8586-666e1815697a
Local pid:
oai:economics.ouls.ox.ac.uk:11901
Deposit date:
2011-08-16