Power Prior for VARs (work in progress, draft available on request)
joint with Massimiliano Marcellino and Tommaso Tornese
Abstract:
Power priors modify the historical likelihood by raising it to the power of a discount factor, allowing researchers to downweight past or external data. This generalizes the standard Bayesian updating formula and provides a flexible framework for incorporating external information into macroeconomic models. We extend the theoretical foundations of power priors to vector autoregressions (VARs) and show that they are equivalent to simple hierarchical structures—while preserving conjugacy and compatibility with modern structural identification methods. Applications include cross-country borrowing to improve forecast accuracy, sharper estimation of long-run Phillips curves, and a novel sensitivity analysis tool based on observation-specific discounting.
Presented at: IAAE 2025 Turin (June 2025)