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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. 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.
  2. William Poole, 2002. "Flation," Speech 49, Federal Reserve Bank of St. Louis.
  3. Athanasios Orphanides & John C. Williams, 2003. "Inflation scares and forecast-based monetary policy," Working Paper 2003-21, Federal Reserve Bank of Atlanta.
  4. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Macroeconomics 0506013, EconWPA.
  5. Bullard, James & Cho, In-Koo, 2003. "Escapist policy rules," CFS Working Paper Series 2003/38, Center for Financial Studies (CFS).
  6. 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.
  7. Orphanides, Athanasios & Williams, John C., 2004. "The decline of activist stabilization policy: natural rate misperceptions, learning, and expectations," Working Paper Series 0337, European Central Bank.
  8. Thomas Sargent & Noah Williams & Tao Zha, 2009. "The Conquest of South American Inflation," Journal of Political Economy, University of Chicago Press, vol. 117(2), pages 211-256, 04.
  9. 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.
  10. Cho, In-Koo & Williams, Noah & Sargent, Thomas J, 2002. "Escaping Nash Inflation," Review of Economic Studies, Wiley Blackwell, vol. 69(1), pages 1-40, January.
  11. 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.
  12. George W. Evans & Seppo Honkapohja & Noah Williams, 2010. "Generalized Stochastic Gradient Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(1), pages 237-262, 02.
  13. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
  14. Milani, Fabio, 2008. "Learning, monetary policy rules, and macroeconomic stability," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3148-3165, October.
  15. Giannitsarou, Chryssi, 2006. "Supply-side reforms and learning dynamics," Journal of Monetary Economics, Elsevier, vol. 53(2), pages 291-309, March.
  16. 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.
  17. 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.
  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. 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.
  20. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April.
  21. repec:dgr:kubcen:199597 is not listed on IDEAS
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