Bayesian inference for the hazard term structure with functional predictors using Bayesian predictive information criteria

A Bayesian method for estimation of a hazard term structure is presented in a functional data analysis framework. The hazard terms structure is designed to include the effects of changes in economic conditions, as well as trends in stock prices and accounting variables from financial statements. The hazard function contains time-varying parameters that are modelled using splines. To estimate the model parameters, a Markov-chain Monte Carlo sampling algorithm is developed. The Bayesian predictive information criterion is employed to assess the default predictive power of the estimated model. The method is then applied to a Japanese firm's default data listed on the Japanese Stock Exchange. The results demonstrate that the proposed method performs well.