Past observation driven changing regime time series models for Forecasting Inflation

In this paper we propose two time series models for inflation modelling. In both models the previous observation plays an important role for the dynamic structure. In the first model we have time-varying autoregressive parameters, which are dependent on the previous observation. The second model is a mixture model, where the regime probabilities are dependent on the previous observation. We compare the forecasts with a random walk model and a time-invariant autoregressive specification. Both models provide solid density forecasts. We find that combining the two models with an equal-weighing scheme, significantly improves the forecast quality.