A Bayesian Simulation Approach to Inference on a Multi-State Latent Factor Intensity Model

Melbourne Institute Working Paper No. 16/08

Date: August 2008


Chew Lian Chua
G. C. Lim
Penelope Smith


This paper provides a Bayesian approach to inference on a multi-state latent factor intensity model to manage the problem of highly analytically intractable pdfs. The sampling algorithm used to obtain posterior distributions of the model parameters includes a particle filter step and a Metropolis-Hastings step within a Gibbs sampler. A simulated example is conducted to show the feasibility and accuracy of this sampling algorithm. The approach is applied to the case of credit ratings transition matrices.

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