Simula@BI: Dynamic Combination and Calibration for Climate Predictions
Speaker: Francesco Ravazzolo
This paper proposes a density calibration and combination model that dynamically calibrate and combine predictive distributions. The time-varying calibration and combination weights are fitted by an observation driven model with dynamics inferred by the score of the assumed conditional likelihood of the data generating process. The model is very flexible and can handle different shapes, instability and model uncertainty. We show this analytically and in simulation exercises. An empirical application to short-term wind speed predictions documents the large instability of individual model performance and their calibration properties, favouring our model in terms of predictive accuracy.