We study 30 vintages of FRB/US, the principal macro model used by the Federal Reserve Board staff for forecasting and … compare these optimal rules against plausible alternatives. Model uncertainty is shown to be a substantial problem; the …

across different horizons and real-time datasets. To further improve performances when forecasting with machine learning, we …

confidence level reflects the decision maker's aversion to statistical uncertainty. The decision rule is applied to a problem of …

probability of decreasing the expected utility becomes greater than the chosen confidence level. The decision maker chooses the …This paper shows how to incorporate judgment in a decision problem under uncertainty, within a classical framework. The … expected utility. The resulting estimator is always at the boundary of the confidence interval: beyond that point the …

confidence level reflects the decision maker’s aversion to statistical uncertainty. The decision rule is applied to a problem of …

decision with a probability equal to the confidence level. For any given prior, there is a mapping from the sample realization … interpreted as decisions under ambiguity …

country modelling. The model converges to a well-defined steady state and its properties are in line with macroeconomic theory … and standard empirical benchmarks. The design is aligned to its role as workhorse model in the context of the forecasting …

-of-sample forecasting performance. Differences across countries are seemingly linked to market liquidity. The paper further finds that the …