Learning from learners
Traditional macroeconomic learning algorithms are misspecified when all agents are learning simultaneously. In this paper, we produce a number of learning algorithms that do not share this failing, and show that this enables them to learn almost any solution, for any parameters, implying learning cannot be used for equilibrium selection. As a by-product, we are able to show that when all agents are learning by traditional methods, all deep structural parameters of standard new-Keynesian models are identified, overturning a key result of Cochrane (2009; 2011). This holds irrespective of whether the central bank is following the Taylor principle, irrespective of whether the implied path is or is not explosive, and irrespective of whether agentsâ€™ beliefs converge. If shocks are observed then this result is trivial, so following Cochrane (2009) our analysis is carried out in the more plausible case in which agents do not observe shocks.
|Date of creation:||Oct 2012|
|Date of revision:|
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NBER Working Papers
15459, National Bureau of Economic Research, Inc.
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School of Economics Discussion Papers
0310, School of Economics, University of Surrey.
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" Saddlepath Learning,"
CDMA Conference Paper Series
0710, Centre for Dynamic Macroeconomic Analysis.
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NBER Working Papers
0684, National Bureau of Economic Research, Inc.
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