Abstract: In recent years, the learnability of rational expectations equilibria (REE)
and determinacy of economic structures have rightfully joined the usual
performance criteria among the sought-after goals of policy design. Some
contributions to the literature, including Bullard and Mitra (2001) and
Evans and Honkapohja (2002), have made significant headway in establishing
certain features of monetary policy rules that facilitate learning. However
a treatment of policy design for learnability in worlds where agents have
potentially misspecified their learning models has yet to surface. This
paper provides such a treatment. We begin with the notion that because the
profession has yet to settle on a consensus model of the economy, it is
unreasonable to expect private agents to have collective rational
expectations. We assume that agents have only an approximate understanding
of the workings of the economy and that their learning the reduced forms of
the economy is subject to potentially destabilizing perturbations. The issue
is then whether a central bank can design policy to account for
perturbations and still assure the learnability of the model. Our test case
is the standard New Keynesian business cycle model. For different
parameterizations of a given policy rule, we use structured singular value
analysis (from robust control theory) to find the largest ranges of
misspecifications that can be tolerated in a learning model without
compromising convergence to an REE.
Keywords: Monetary policy, learning, e-stability, learnability, robust control
Full paper (590 KB PDF)
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Last update: December 15, 2005
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