Sampling Variability and Estimated Forecast Combinations

A forecast combination is produced by taking a weighted average of forecasts from different sources, such as different statistical models. This thesis proposes a novel method for the production of forecast combinations, and shows that this method has superior predictive accuracy relative to the traditional method most commonly used today. This finding is driven primarily by the relationship between sampling variability and predictive accuracy. A systematic exploration and comparison of these two methods is undertaken based on statistical theory, Monte Carlo simulation, and empirical data, with several implications arising for the use of forecast combination techniques in practice.