Bayesian Inference about the Types of Structural Breaks When There Are Different Breaks in Many Parameters

I propose a Bayesian approach to making an inference about complicated patterns of structural breaks in time series. Structural break models in the literature are mainly considered for a simple case in which all the parameters under the structural changes are restricted to have breaks at the same dates. Unlike the existing literature, the proposed method in this paper allows multiple parameters such as intercept, persistence, and/or residual variance to undergo mutually independent structural breaks at different dates with the different number of breaks across parameters. To estimate the complex structural break models considered in this paper, structural breaks in the multiple parameters are interpreted as regime transitions. The regime for each parameter is then indicated by a corresponding discrete latent variable which follows a first-order Markov process. A Markov-chain Monte Carlo scheme is developed to estimate and compare the complex structural break models, which are potentially non-nested, in an efficient and tractable way. I then apply this approach to postwar U.S. inflation and find strong support for an autoregressive model with two structural breaks in residual variance and no break in intercept and persistence