A Bayesian Simulation Approach to Inference on a Multi-State Latent Factor Intensity Model
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.
|Date of creation:||Aug 2008|
|Date of revision:|
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