Forecasting Under Structural Change

Forecasting strategies that are robust to structural breaks have earned renewed attention in the literature. They are built on weighted averages downweighting past information and include forecasting with rolling window, exponential smoothing or exponentially weighted moving average and forecast pooling. These simple strategies are particularly attractive because they are easy to implement, possibly robust to different types of structural change and can adjust for breaks in real time. This review introduces the dynamic model to be forecast, explains in detail how the data-dependent tuning parameter for discounting the past data is selected and how basic forecasts are constructed and the forecast error estimated. It comments on the forecast error and the impact of weak and strong dependence of noise on the quality of the prediction. It also describes various forecasting methods and evaluates their practical performance in robust forecasting.

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