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Adaptive Learning in Practice

  • Carceles-Poveda, Eva
  • Giannitsarou, Chryssi

We analyse some practical aspects of implementing adaptive learning in the context of forward-looking linear models. In particular, we focus on how to set initial conditions for three popular algorithms, namely recursive least squares, stochastic gradient and constant gain learning. We propose three ways of initializing, one that uses randomly generated data, a second that is ad-hoc and a third that uses an appropriate distribution. We illustrate, via standard examples, that the behaviour and evolution of macroeconomic variables not only depend on the learning algorithm, but on the initial conditions as well. Furthermore, we provide a computing toolbox for analysing the quantitative properties of dynamic stochastic macroeconomic models under adaptive learning.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 5627.

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Date of creation: Apr 2006
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Handle: RePEc:cpr:ceprdp:5627
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  1. Athanasios Orphanides & John C. Williams, 2003. "The decline of activist stabilization policy: natural rate misperceptions, learning, and expectations," Working Paper Series 2003-24, Federal Reserve Bank of San Francisco.
  2. William Poole & Robert H. Rasche, 2002. "Flation," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 1-6.
    • William Poole, 2002. "Flation," Speech 49, Federal Reserve Bank of St. Louis.
  3. Chryssi Giannitsarou, 2004. "Supply-side reforms and learning dynamics," Money Macro and Finance (MMF) Research Group Conference 2003 36, Money Macro and Finance Research Group.
  4. Eva Carceles-Poveda & Chryssi Giannitsarou, 2008. "Asset Pricing with Adaptive Learning," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 11(3), pages 629-651, July.
  5. John Y. Campbell, 1992. "Inspecting the Mechanism: An Analytical Approach to the Stochastic Growth Model," NBER Working Papers 4188, National Bureau of Economic Research, Inc.
  6. Albert Marcet & Juan P. Nicolini, 1995. "Recurrent hyperinflations and learning," Economics Working Papers 244, Department of Economics and Business, Universitat Pompeu Fabra, revised Nov 2001.
  7. McCallum, Bennett T., 2007. "E-stability vis-a-vis determinacy results for a broad class of linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 31(4), pages 1376-1391, April.
  8. James Bullard & Stefano Eusepi, 2003. "Did the Great Inflation Occur Despite Policymaker Commitment to a Taylor Rule?," Computing in Economics and Finance 2003 129, Society for Computational Economics.
  9. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
  10. Thomas Sargent & Noah Williams & Tao Zha, 2006. "The conquest of South American inflation," Working Paper 2006-20, Federal Reserve Bank of Atlanta.
  11. 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.
  12. Athanasios Orphanides & John C. Williams, 2003. "Inflation scares and forecast-based monetary policy," Finance and Economics Discussion Series 2003-41, Board of Governors of the Federal Reserve System (U.S.).
  13. George W. Evans & Seppo Honkapohja & Noah Williams, 2005. "Generalized Stochastic Gradient Learning," University of Oregon Economics Department Working Papers 2005-17, University of Oregon Economics Department, revised 18 May 2008.
  14. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April.
  15. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
  16. Milani, Fabio, 2008. "Learning, monetary policy rules, and macroeconomic stability," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3148-3165, October.
  17. Evans, George W. & Honkapohja, Seppo, 1998. "Convergence of learning algorithms without a projection facility," Journal of Mathematical Economics, Elsevier, vol. 30(1), pages 59-86, August.
  18. Cho, In-Koo & Sargent, Thomas J., 2000. "Escaping Nash inflation," Working Paper Series 0023, European Central Bank.
  19. Bullard, James & Cho, In-Koo, 2003. "Escapist policy rules," CFS Working Paper Series 2003/38, Center for Financial Studies (CFS).
  20. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Working Papers 050607, University of California-Irvine, Department of Economics.
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