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Adaptive Learning in Practice

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Author Info
Carceles-Poveda, Eva
Giannitsarou, Chryssi

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Abstract

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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Related research
Keywords: adaptive learning; computational methods; least square estimations; short-run dynamics;

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Find related papers by JEL classification:
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General

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References listed on IDEAS
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. 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)
    Other versions:
  3. Thomas Sargent & Noah Williams & Tao Zha, 2006. "The Conquest of South American Inflation," NBER Working Papers 12606, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  4. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April. [Downloadable!]
  5. 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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  6. 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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  7. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Macroeconomics 0506013, EconWPA. [Downloadable!]
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  8. 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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  9. 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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  10. 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)
  11. 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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  12. 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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  13. 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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  14. 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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  15. Albert Marcet & Juan P. Nicolini, 2003. "Recurrent Hyperinflations and Learning," American Economic Review, American Economic Association, vol. 93(5), pages 1476-1498, December. [Downloadable!]
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  16. 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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  17. Fabio Milani, 2005. "Expectations, Learning and Macroeconomic Persistence," Macroeconomics 0510022, EconWPA. [Downloadable!]
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  18. Fabio Milani, 2005. "Learning, Monetary Policy Rules, and Macroeconomic Stability," Macroeconomics 0508019, EconWPA. [Downloadable!]
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  19. 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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Full references

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. 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:
  2. Kevin X.D. Huang & Zheng Liu & Tao Zha, 2008. "Learning, adaptive expectations, and technology shocks," Working Paper 2008-20, Federal Reserve Bank of Atlanta. [Downloadable!]
    Other versions:
  3. 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!]
    Other versions:
  4. Pfajfar, D. & Santoro, E., 2008. "Asymmetries in Inflation Expectation Formation Across Demographic Groups," Cambridge Working Papers in Economics 0824, Faculty of Economics, University of Cambridge. [Downloadable!]
  5. Fanelli, Luca, 2008. "Evaluating New Keynesian Phillips Curve under VAR-Based Learning," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy, vol. 2(33), pages 1-24. [Downloadable!]
  6. 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!]
  7. 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!]
  8. 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!]
  9. Orlando Gomes, 2008. "Stability under Learning: the Endogenous Growth Problem," Working Papers ercwp1708, ISCTE, UNIDE, Economics Research Centre. [Downloadable!]
  10. Pfajfar, D. & Zakelj, B., 2009. "Experimental Evidence on Inflation Expectation Formation," Discussion Paper 2009-07, Tilburg University, Center for Economic Research. [Downloadable!]
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