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Learning from learners

Author Info

Listed author(s):
  • Tom Holden

    (University of Surrey)

Abstract

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 agents’ 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.

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File URL: http://www.fahs.surrey.ac.uk/economics/discussion_papers/2012/DP15-12.pdf
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Paper provided by School of Economics, University of Surrey in its series School of Economics Discussion Papers with number 1512.

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Length: 39 pages
Date of creation: Oct 2012
Handle: RePEc:sur:surrec:1512
Contact details of provider: Postal:
Guildford, Surrey GU2 5XH

Phone: (01483) 259380
Fax: (01483) 259548
Web page: http://www.surrey.ac.uk/economics/
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Keywords:

Find related papers by JEL classification:

  • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
  • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
  • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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  1. Ellison, Martin & Pearlman, Joseph, 2011. "Saddlepath learning," Journal of Economic Theory, Elsevier, vol. 146(4), pages 1500-1519, July.
  2. Holden, Tom, 2008. "Rational macroeconomic learning in linear expectational models," MPRA Paper 10872, University Library of Munich, Germany.
  3. Lubik, Thomas A. & Schorfheide, Frank, 2003. "Computing sunspot equilibria in linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 28(2), pages 273-285, November.
  4. Pearlman, Joseph & Currie, David & Levine, Paul, 1986. "Rational expectations models with partial information," Economic Modelling, Elsevier, vol. 3(2), pages 90-105, April.
  5. Blanchard, Olivier Jean & Kahn, Charles M, 1980. "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, vol. 48(5), pages 1305-1311, July.
  6. Paul Levine & Joseph Pearlman & George Perendia & Bo Yang, 2012. "Endogenous Persistence in an estimated DSGE Model Under Imperfect Information," Economic Journal, Royal Economic Society, vol. 122(565), pages 1287-1312, December.
  7. Norbert Christopeit & Michael Massmann, 2010. "Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning," Tinbergen Institute Discussion Papers 10-077/4, Tinbergen Institute.
  8. Cochrane, John H., 2009. "Can learnability save new-Keynesian models?," Journal of Monetary Economics, Elsevier, vol. 56(8), pages 1109-1113, November.
  9. McCallum, Bennett T., 1983. "On non-uniqueness in rational expectations models : An attempt at perspective," Journal of Monetary Economics, Elsevier, vol. 11(2), pages 139-168.
  10. Gabrielsen, Arne, 1978. "Consistency and identifiability," Journal of Econometrics, Elsevier, vol. 8(2), pages 261-263, October.
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