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Finance and Economics Discussion Series: Activist vs. Non-Activist Monetary Policy: Optimal Rules Under Extreme Uncertainty (en Inglés)
United States Federal Reserve Board
(Autor)
·
Peter Von Zur Muehlen
(Autor)
·
Bibliogov
· Tapa Blanda
Finance and Economics Discussion Series: Activist vs. Non-Activist Monetary Policy: Optimal Rules Under Extreme Uncertainty (en Inglés) - Von Zur Muehlen, Peter ; United States Federal Reserve Board
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Reseña del libro "Finance and Economics Discussion Series: Activist vs. Non-Activist Monetary Policy: Optimal Rules Under Extreme Uncertainty (en Inglés)"
This paper analyzes the optimality of reactive feedback rules advocated by neo-Keynesians, and constant money growth rules proposed by monetarists. The basis for this controversy is not merely a disagreement concerning sources and impacts of uncertainty in the economy, but also an apparent fundamental difference in the attitude toward uncertainty about models. To address these differences, this paper compares the relative reactiveness of a monetary policy instrument to conditioning information for two starkly differing versions of model uncertainty about the model and the data driving it: Bayesian uncertainty that assumes known probability distributions for a model's parameters and the data Knightian uncertainty that does not. In the latter case, the policy maker copes with extreme uncertainty by playing a mental game against "natuare," using minmax strategies. Contrary to common intuition, extreme uncertainty about a model's parameters does not necessarily imply less responsiveness to conditioning information--here represented by the lagged gap between nominal income growth and its trend--and it certainly does not justify constancy of money growth except in an extreme version of Brainard's (1967) result. A partial constant growth rule can be derived in only one special case: if the conditioning variable in the feedback rule is also uncertain in either Bayesian or Knightian senses and the authority used Neyman-Pearson likelihood ratio tests to distinguish noise from information with each new observation.
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