A self-tuning model for inflation rate dynamics

A regime-switching model is proposed to capture the structural changes in inflation dynamics. We apply a special version of the EM algorithm to find optimal parameter estimates of the model within the framework of a discrete-time finite state Markov chain that governs the switching of regimes from one state to another. The model is implemented to Canada’s consumer price index (CPI) data series and its performance is assessed by comparing its one-step ahead predictions with the actual data. We found that, within the dataset studied, a two-state Markov-switching model is sufficient to capture the dynamics of Canadian CPI series. The model being proposed is adaptive as parameters are updated upon the arrival of a new set of information. A description of how to calculate the standard errors for parameter estimates using the Fisher information matrix is provided. We also determine the optimal number of states for the Markov chain within the dataset considered via the AIC analysis.