A Bayesian Simulation Approach To Inference On A Multi-State Latent Factor Intensity Model

Chew Lian Chua, G. C. Lim, Penelope Smith

Research output: Journal PublicationArticlepeer-review


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.

Original languageEnglish
Pages (from-to)179-195
Number of pages17
JournalAustralian and New Zealand Journal of Statistics
Issue number2
Publication statusPublished - Jun 2011
Externally publishedYes


  • Auxiliary particle filter
  • Latent factor model
  • Non-linear non-Gaussian state space model
  • Transitions in credit ratings

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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