Working Paper: CEPR ID: DP6158
Authors: Graham Elliott; Allan Timmermann
Abstract: Forecasts guide decisions in all areas of economics and finance and their value can only be understood in relation to, and in the context of, such decisions. We discuss the central role of the loss function in helping determine the forecaster's objectives and use this to present a unified framework for both the construction and evaluation of forecasts. Challenges arise from the explosion in the sheer volume of predictor variables under consideration and the forecaster's ability to entertain an endless array of functional forms and time-varying specifications, none of which may coincide with the `true' model. Methods for comparing the forecasting performance of pairs of models or evaluating the ability of the best of many models to beat a benchmark specification are also reviewed.
Keywords: economic forecasting; forecast evaluation; loss function
JEL Codes: C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| loss function (J17) | forecaster's objectives (C53) |
| forecasting models (C53) | economic decisions (G11) |
| choice of predictor variables (C52) | forecasting outcomes (C53) |
| overfitting (C52) | forecasting performance (C53) |
| historical track record (N70) | effectiveness of forecasting model (C53) |
| parameter instability (C62) | poor forecasts (G17) |