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Adaptive Learning in Practice

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  • Chryssi Giannitsarou
  • Eva Carceles-Poveda

Abstract

While there is an extensive literature on identifying the asymptotic properties of adaptive learning algorithms, little is explicitly mentioned on how to actually implement these algorithms on the computer to analyze the quantitative effects of learning in dynamic macroeconomic models. The aim of this paper is twofold. First, we provide a detailed practical description of how to numerically implement least squares learning in the context of a reduced form forward looking model with an endogenous lag. Second, while we give a brief overview of the asymptotic properties of least squares learning for the reduced form at hand, the analysis focuses on illustrating the importance of the initial conditions of the learning algorithm for the study of medium and short run dynamics. In particular, we propose and discuss two ways of initializing the algorithm, one that is based on randomly generated data and a second that is ad-hoc. Using several variations of the basic real business cycle model, we then compare the behavior of the variables of interest for a variety of initializations. Our results indicate that, for short time horizons of up to 300 periods (corresponding to 75 years of quarterly data), the evolution of aggregate variables depends crucially on the initial conditions of the algorithm, and the learning dynamics might deviate significantly from the corresponding rational expectations case depending on the initialization.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 271.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:271

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Keywords: Adaptive learning; least squares estimation; computational methods; short-run dynamics;

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References

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  1. James B. Bullard & Stefano Eusepi, 2004. "Did the Great Inflation occur despite policymaker commitment to a Taylor rule?," Working Papers 2003-013, Federal Reserve Bank of St. Louis.
  2. In-Koo Cho & Noah Williams & Thomas J. Sargent, 2002. "Escaping Nash Inflation," Review of Economic Studies, Oxford University Press, vol. 69(1), pages 1-40.
  3. Carceles-Poveda, Eva & Giannitsarou, Chryssi, 2007. "Asset Pricing with Adaptive Learning," CEPR Discussion Papers 6223, C.E.P.R. Discussion Papers.
  4. 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.
  5. Thomas Sargent & Noah Williams & Tao Zha, 2006. "The Conquest of South American Inflation," NBER Working Papers 12606, National Bureau of Economic Research, Inc.
  6. Marcet, Albert & Nicolini, Juan Pablo, 1998. "Recurrent Hyperinflations and Learning," CEPR Discussion Papers 1875, C.E.P.R. Discussion Papers.
  7. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
  8. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Working Papers 050607, University of California-Irvine, Department of Economics.
  9. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April.
  10. George W. Evans & Seppo Honkapohja & Noah Williams, 2005. "Generalized Stochastic Gradient Learning," CESifo Working Paper Series 1576, CESifo Group Munich.
  11. James Bullard & In-Koo Cho, 2003. "Escapist policy rules," Working Papers 2002-002, Federal Reserve Bank of St. Louis.
  12. Uhlig, H., 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper 1995-97, Tilburg University, Center for Economic Research.
  13. Athanasios Orphanides & John C. Williams, 2005. "Inflation scares and forecast-based monetary policy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 498-527, April.
  14. Campbell, John, 1994. "Inspecting the Mechanism: An Analytical Approach to the Stochastic Growth Model," Scholarly Articles 3196342, Harvard University Department of Economics.
  15. Fabio Milani, 2005. "Learning, Monetary Policy Rules, and Macroeconomic Stability," Macroeconomics 0508019, EconWPA.
  16. Eva Carceles-Poveda & Chryssi Giannitsarou, 2007. "Online Appendix to Asset Pricing with Adaptive Learning," Technical Appendices carceles08, Review of Economic Dynamics.
  17. 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.
  18. 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.
  19. Bennett T. McCallum, 1981. "On Non-Uniqueness in Rational Expectations Models: An Attempt at Perspective," NBER Working Papers 0684, National Bureau of Economic Research, Inc.
  20. William Poole, 2002. "Flation," Speech 49, Federal Reserve Bank of St. Louis.
  21. 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.
  22. 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.
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