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Adaptive Learning in Practice

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Author Info
Chryssi Giannitsarou
Eva Carceles-Poveda

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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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Publisher 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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Related research
Keywords: Adaptive learning least squares estimation computational methods short-run dynamics

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Find related papers by JEL classification:
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

References listed on IDEAS
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  1. Evans, G.W. & Honkapohja ,S. & Williams, N., 2005. "Generalized Stochastic Gradient Learning," Cambridge Working Papers in Economics 0545, Faculty of Economics, University of Cambridge. [Downloadable!]
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  2. Orphanides, Athanasios & Williams, John C, 2005. "Inflation Scares and Forecast-Based Monetary Policy," CEPR Discussion Papers 4844, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  3. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April. [Downloadable!]
  4. Bennett T. McCallum, 2006. "E-Stability vis-a-vis Determinacy Results for a Broad Class of Linear Rational Expectations Models," NBER Working Papers 12441, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  5. Uhlig, H., 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper 97, Tilburg University, Center for Economic Research. [Downloadable!]
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  6. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Macroeconomics 0506013, EconWPA. [Downloadable!]
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  7. Eva Carceles Poveda & Chryssi Giannitsarou, 2006. "Asset pricing with adaptive learning," Computing in Economics and Finance 2006 25, Society for Computational Economics. [Downloadable!]
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  8. Bennett T. McCallum, 1983. "On Non-Uniqueness in Rational Expectations Models: An Attempt at Perspective," NBER Working Papers 0684, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  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. [Downloadable!] (restricted)
  10. Campbell, John Y., 1994. "Inspecting the mechanism: An analytical approach to the stochastic growth model," Journal of Monetary Economics, Elsevier, vol. 33(3), pages 463-506, June. [Downloadable!] (restricted)
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  11. Bullard, James & Cho, In-Koo, 2005. "Escapist policy rules," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1841-1865, November. [Downloadable!] (restricted)
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  12. James Bullard & Stefano Eusepi, 2005. "Did the Great Inflation Occur Despite Policymaker Commitment to a Taylor Rule?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 324-359, April. [Downloadable!] (restricted)
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  13. Cho, In-Koo & Williams, Noah & Sargent, Thomas J, 2002. "Escaping Nash Inflation," Review of Economic Studies, Blackwell Publishing, vol. 69(1), pages 1-40, January.
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  14. Albert Marcet & Juan P. Nicolini, 2003. "Recurrent Hyperinflations and Learning," American Economic Review, American Economic Association, vol. 93(5), pages 1476-1498, December. [Downloadable!] (restricted)
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  15. Giannitsarou, Chryssi, 2006. "Supply-side reforms and learning dynamics," Journal of Monetary Economics, Elsevier, vol. 53(2), pages 291-309, March. [Downloadable!] (restricted)
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  16. Fabio Milani, 2005. "Expectations, Learning and Macroeconomic Persistence," Macroeconomics 0510022, EconWPA. [Downloadable!]
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  17. Fabio Milani, 2005. "Learning, Monetary Policy Rules, and Macroeconomic Stability," Macroeconomics 0508019, EconWPA. [Downloadable!]
  18. 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. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Damjan Pfajfar & Emiliano Santoro, 2007. "Heterogeneity, Asymmetries and Learning in InfIation Expectation Formation: An Empirical Assessment," Money Macro and Finance (MMF) Research Group Conference 2006 123, Money Macro and Finance Research Group. [Downloadable!]
  2. Kevin X.D. Huang & Zheng Liu & Tao Zha, 2008. "Learning, adaptive expectations, and technology shocks," Working Paper Series 2008-18, Federal Reserve Bank of San Francisco. [Downloadable!]
  3. Eva Carceles Poveda & Chryssi Giannitsarou, 2006. "Asset pricing with adaptive learning," Computing in Economics and Finance 2006 25, Society for Computational Economics. [Downloadable!]
    Other versions:
  4. Fanelli, Luca, 2008. "Evaluating the New Keynesian Phillips Curve under VAR-Based Learning," Economics Discussion Papers 2008-15, Kiel Institute for the World Economy. [Downloadable!]
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  5. James Murray, 2008. "Empirical Significance of Learning in a New Keynesian Model with Firm-Specific Capital," Caepr Working Papers 2007-027, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington. [Downloadable!]
  6. Emiliano Santoro & Damjan Pfajfar, 2006. "Heterogeneity and learning in inflation expectation formation: an empirical assessment," Department of Economics Working Papers 0607, Department of Economics, University of Trento, Italia. [Downloadable!]
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