Working Paper: CEPR ID: DP5361
Authors: Allan G. Timmermann
Abstract: Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the degree of correlation between forecast errors and the relative size of the individual models? forecast error variances). Although the reasons for the success of simple combination schemes are poorly understood, we discuss several possibilities related to model misspecification, instability (non-stationarities) and estimation error in situations where the numbers of models is large relative to the available sample size. We discuss the role of combinations under asymmetric loss and consider combinations of point, interval and probability forecasts.
Keywords: diversification; gains; forecast combinations; model misspecification; pooling; trimming; shrinkage methods
JEL Codes: C22; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Combining forecasts (C53) | Increased forecast accuracy (C53) |
| Degree of correlation between forecast errors (C53) | Advantages of combining forecasts (C53) |
| Forecast combinations mitigate effects of model misspecification (C53) | More robust predictions (C53) |
| Forecast combinations mitigate effects of instability (C53) | More robust predictions (C53) |