This paper considers combining forecasts generated from the same model but over different estimation windows. It develops theoretical results for random walks with breaks in the drift and volatility and for a linear regression model with a break in the slope parameter. Averaging forecasts over different estimation windows leads to a lower bias and root mean square forecast error than forecasts based on a single estimation window for all but the smallest breaks. An application to weekly returns on 20 equity index futures shows that averaging forecasts over estimation windows leads to a smaller RMSFE than some competing methods.
- C22: Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models &bull Diffusion Processes
- C53: Forecasting and Prediction Methods • Simulation Methods