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Noah Williams

1 August 2002
Recently there has been much interest in studying monetary policy under model uncertainty. We develop methods to analyze different sources of uncertainty in one coherent structure useful for policy decisions. We show how to estimate the size of the uncertainty based on time series data, and incorporate this uncertainty in policy optimization. We propose two different approaches to modeling model uncertainty. The first is model error modeling, which imposes additional structure on the errors of an estimated model, and builds a statistical description of the uncertainty around a model. The second is set membership identification, which uses a deterministic approach to find a set of models consistent with data and prior assumptions. The center of this set becomes a benchmark model, and the radius measures model uncertainty. Using both approaches, we compute the robust monetary policy under different model uncertainty specifications in a small model of the US economy.
JEL Code
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
D81 : Microeconomics→Information, Knowledge, and Uncertainty→Criteria for Decision-Making under Risk and Uncertainty
International Seminar on Macroeconomics