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 …

I study 46 vintages of FRB/US, the principal macro model used by Federal Reserve Board staff for forecasting and policy … analysis, as measures of real-time model uncertainty. I also study the implications of model uncertainty for the robustness of … commonly applied, simple monetary policy rules. I first document that model uncertainty poses substantial challenges for …

I study 46 vintages of FRB/US, the principal macro model used by Federal Reserve Board staff for forecasting and policy … analysis, as measures of real-time model uncertainty. I also study the implications of model uncertainty for the robustness of … commonly applied, simple monetary policy rules. I first document that model uncertainty poses substantial challenges for …

probability of default through binary logistic regression model and determining optimum parameters that minimize the objective …

We develop a transparent Bayesian approach to quantify uncertainty about linear stochastic discount factor models. We … show that, for a Bayesian decision-maker, the model probability decreases with historical maximum in-sample Sharpe ratios … and increases with model dimensions. We apply our approach to quantify the time series of model uncertainty in North …

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 …