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

43 Pages Posted: 27 Jan 2004

See all articles by Fabio Milani

Fabio Milani

University of California, Irvine - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: August 2002

Abstract

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.

Keywords: Bayesian model averaging, leading indicators, model uncertainty, optimal monetary policy, interest rate smoothing

JEL Classification: C11, C15, C52, E52, E58

Suggested Citation

Milani, Fabio, Monetary Policy with a Wider Information Set: A Bayesian Model Averaging Approach (August 2002). Available at SSRN: https://ssrn.com/abstract=490082 or http://dx.doi.org/10.2139/ssrn.490082

Fabio Milani (Contact Author)

University of California, Irvine - Department of Economics ( email )

3151 Social Science Plaza
Irvine, CA 92697-5100
United States

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