Information Theory and Ensemble Models

Forecasting economic variables like inflation is challenging because traditional accuracy metrics often fail to differentiate between the performance of various models. This makes it difficult to select a single best model or create an effective ensemble. The content proposes a novel framework for ensembling time-series forecasts by applying concepts from information theory. Instead of relying on distance-based metrics, this approach uses Shannon entropy, calculated from the spectral density of the data, to quantify the information captured by a model. By measuring the information content, it becomes possible to create more sophisticated and effective ensemble weighting schemes.