A robust approach to tilting: parametric relative entropy

We introduce a novel methodology, ”parametric tilting,” for incorporating external information into econometric model-based density forecasts. Unlike traditional entropic tilting, which can generate unrealistic or unstable distributions under certain conditions, parametric tilting ensures more reliable and numerically stable results. Our approach leverages the flexibility of the skew-T distribution, which captures key moments of macroeconomic time series, and minimizes the Kullback-Leibler divergence between the target and model-based distributions. This method overcomes limitations of entropic tilting, such as multimodal or degenerate distributions, providing a robust alternative for policymakers and researchers aiming to integrate external views into probabilistic forecasting frameworks. JEL Classification: C14, C53, E52