Template-type: ReDIF-Paper 1.0
Author-Name: Siddhartha Chib
Author-Workplace-Name: Olin School of Business, Washington University
Author-Name: Michael K Pitt
Author-Workplace-Name: University of Warwick
Author-Name: Neil Shephard
Author-Email: neil.shephard@nuffield.ox.ac.uk
Author-Workplace-Name: Nuffield College, University of Oxford, UK
Title: Likelihood based inference for diffusion driven models
Abstract: This paper provides methods for carrying out likelihood based inference for
diffusion driven models, for example discretely observed multivariate
diffusions, continuous time stochastic volatility models and counting
process models. The diffusions can potentially be non-stationary. Although
our methods are sampling based, making use of Markov chain Monte Carlo
methods to sample the posterior distribution of the relevant unknowns, our
general strategies and details are different from previous work along these
lines. The methods we develop are simple to implement and simulation
efficient. Importantly, unlike previous methods, the performance of our
technique is not worsened, in fact it improves, as the degree of latent
augmentation is increased to reduce the bias of the Euler approximation. In
addition, our method is not subject to a degeneracy that afflicts previous
techniques when the degree of latent augmentation is increased. We also
discuss issues of model choice, model checking and filtering. The techniques
and ideas are applied to both simulated and real data.
Keywords:  Bayes estimation, Brownian bridge, Non-linear diffusion,
Euler approximation, Markov chain Monte Carlo, Metropolis-Hastings
algorithm, Missing data, Simulation, Stochastic differential equation.
Length: 25 pages
Creation-Date:2004-08-22
Number:2004-W20
File-URL: http://www.nuff.ox.ac.uk/economics/papers/2004/w20/chibpittshephard.pdf
File-Format: application/pdf
Handle: RePEc:nuf:econwp:0420