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Monetary Policy with a Wider Information Set: a Bayesian Model Averaging Approach

  • Fabio Milani

    (Princeton University)

Monetary policy has been usually analyzed in the context of small macroeconomic models where central banks are allowed to exploit a limited amount of information. Under these frameworks, researchers typically derive the optimality of aggressive monetary rules, contrasting with the observed policy conservatism and interest rate smoothing. This paper allows the central bank to exploit a wider information set, while taking into account the associated model uncertainty, by employing Bayesian Model Averaging with Markov Chain Model Composition (MC³). In this enriched environment, we derive the optimality of smoother and more cautious policy rates, together with clear gains in macroeconomic efficiency.

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Paper provided by EconWPA in its series Macroeconomics with number 0401004.

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Length: 43 pages
Date of creation: 18 Jan 2004
Date of revision:
Handle: RePEc:wpa:wuwpma:0401004
Note: Type of Document - pdf; prepared on Win2000; pages: 43; figures: included
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  14. Favero, Carlo A. & Milani, Fabio, 2005. "Parameter Instability, Model Uncertainty and the Choice of Monetary Policy," CEPR Discussion Papers 4909, C.E.P.R. Discussion Papers.
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  29. repec:cup:macdyn:v:6:y:2002:i:1:p:85-110 is not listed on IDEAS
  30. Fabio Milani, 2003. "Adaptive Learning, Model Uncertainty and Monetary Policy Inertia in a Large Information Environment," Computing in Economics and Finance 2003 280, Society for Computational Economics.
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