Forecasting Inflation in the Presence of Structural Breaks: A Time‐Varying Parameter Approach

This paper extends the work of Hall et al. (2025), who demonstrated that, while structural break tests for an unknown break date are unable to detect structural breaks near the end of a sample period, they can be effective when the user has a prior expectation of where the break occurs. In this paper, we demonstrate that a time‐varying parameter forecasting model can also be effective when we know approximately where the break occurs. We use a Kalman filter time‐varying AR forecasting rule, where the degree of time variation is governed by the Q‐matrix. We provide evidence that this is a better formulation than the standard rolling window approach in the literature.