Using Reversible Jump MCMC to Account for Model Uncertainty
نویسندگان
چکیده
When fitting a model to any data, there is some uncertainty about which model is best. Green (1995) quantifies this uncertainty through the Reversible Jump Markov Chain Monte Carlo (RJMCMC) method. When using the RJMCMC method in a regime-switching situation, the chain determines the optimal number of regimes by jumping between various possibilities. This method gives each model its posterior probability of being the best. After an overview of the methodology, we apply it to various datasets and discuss the applications in modern actuarial science.
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