Melbourne Institute of Applied Economic and Social Research, University of Melbourne, Parkville, Vic. 3010, Australia. e-mail: firstname.lastname@example.org
A BAYESIAN SIMULATION APPROACH TO INFERENCE ON A MULTI-STATE LATENT FACTOR INTENSITY MODEL
Article first published online: 28 SEP 2011
© 2011 Australian Statistical Publishing Association Inc.
Australian & New Zealand Journal of Statistics
Volume 53, Issue 2, pages 179–195, June 2011
How to Cite
Chua, C. L., Lim, G.C. and Smith, P. (2011), A BAYESIAN SIMULATION APPROACH TO INFERENCE ON A MULTI-STATE LATENT FACTOR INTENSITY MODEL. Australian & New Zealand Journal of Statistics, 53: 179–195. doi: 10.1111/j.1467-842X.2011.00625.x
- Issue published online: 20 OCT 2011
- Article first published online: 28 SEP 2011
- auxiliary particle filter;
- latent factor model;
- non-linear non-Gaussian state space model;
- transitions in credit ratings
The influence of economic conditions on the movement of a variable between states (for example a change in credit rating from A to B) can be modelled using a multi-state latent factor intensity framework. Estimation of this type of model is, however, not straightforward, as transition probabilities are involved and the model contains a few highly analytically intractable distributions. In this paper, a Bayesian approach is adopted to manage the distributions. The innovation in the sampling algorithm used to obtain the posterior distributions of the model parameters includes a particle filter step and a Metropolis–Hastings step within a Gibbs sampler. The feasibility and accuracy of the proposed sampling algorithm is supported with a few simulated examples. The paper contains an application concerning what caused 1049 firms to change their credit ratings over a span of ten years.