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Monetary policy in a model with misspecified, heterogeneous and ever-changing expectations

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  • Alberto Locarno

    ()
    (Bank of Italy)

Abstract

The applied literature on adaptive learning has mostly focused on small, linear models, with homogenous expectations. In non-linear models heterogeneous expectations prevail and the process through which agents select (and change) a forecasting model becomes a necessary ingredient of the analysis; moreover, the temporary equilibrium of the learning process approaches an asymptotic limit that may be affected by the communication strategies of the monetary policymaker. The objective of this paper is to assess whether in such a model economy the optimal monetary policy exhibits properties that are similar to those found in the literature for small, linear models. The main results are the following: (1) expectations heterogeneity is an intrinsic feature of the economy: no PLM succeeds in ruling out all the other forecasting models; (2) contrary to previous findings, the monetary policymaker has no incentive to adopt highly inflation-averse policies: too strong a reaction to price shocks increases both inflation and output volatility; (3) partial transparency seems to enhance somewhat welfare (but fully transparent policies do not); (4) a higher degree of transparency calls for stronger inflation aversion.

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Bibliographic Info

Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 888.

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Date of creation: Oct 2012
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Handle: RePEc:bdi:wptemi:td_888_12

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Keywords: Bounded rationality; generalised stochastic gradient learning; transparency.;

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  15. Berardi, Michele & Duffy, John, 2007. "The value of central bank transparency when agents are learning," European Journal of Political Economy, Elsevier, vol. 23(1), pages 9-29, March.
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