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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 policymakers in that key model properties differ in important ways across model vintages. Then I show that the parameterization of optimized simple policy rule-rules that are intended to be robust with respect to model uncertainty-also differ substantially across model vintages. Included in the set of rules are rules that eschew feedback on the output gap, rules that target nominal income growth, and rules that allow for time variation in the equilibrium real interest rate. I find that many rules, which previous research has shown to be robust in artificial economies, would have failed to provide adequate stabilization in the real-time, real-world environment seen by the Fed staff. However, I do identify certain policy rules that would have performed relatively well, and I characterize the key features of those rules to draw more general lessons about the design of monetary policy under model uncertainty. [ABSTRACT FROM AUTHOR]

We study 30 vintages of FRB/US, the principal macro model used by the Federal Reserve Board staff for forecasting and policy analysis. To do this, we exploit archives of the model code, coefficients, baseline databases and stochastic shock sets stored after each FOMC meeting from the model's inception in July 1996 until November 2003. The period of study was one of important changes in the U.S. economy with a productivity boom, a stock market boom and bust, a recession, the Asia crisis, the Russian debt default, and an abrupt change in fiscal policy. We document the surprisingly large and consequential changes in model properties that occurred during this period and compute optimal Taylor-type rules for each vintage. We compare these optimal rules against plausible alternatives. Model uncertainty is shown to be a substantial problem; the efficacy of purportedly optimal policy rules should not be taken on faith. We also find that previous findings that simple rules are robust to model uncertainty may be an overly sanguine conclusion. [ABSTRACT FROM AUTHOR]