IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this paper

Adaptive Learning in Practice

Listed author(s):
  • Chryssi Giannitsarou
  • Eva Carceles-Poveda

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.

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.

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

as
in new window

Length:
Date of creation: 11 Aug 2004
Handle: RePEc:sce:scecf4:271
Contact details of provider: Web page: http://comp-econ.org/
Email:


More information through EDIRC

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.:

as
in new window


  1. 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.
  2. 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.
  3. Eva Carceles Poveda & Chryssi Giannitsarou, 2006. "Asset pricing with adaptive learning," Computing in Economics and Finance 2006 25, Society for Computational Economics.
  4. Athanasios Orphanides & John C. Williams, 2003. "The decline of activist stabilization policy: natural rate misperceptions, learning, and expectations," Proceedings, Board of Governors of the Federal Reserve System (U.S.).
  5. Thomas J. Sargent & Noah Williams & Tao Zha, 2006. "The conquest of South American inflation," FRB Atlanta Working Paper 2006-20, Federal Reserve Bank of Atlanta.
  6. 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.
  7. Athanasios Orphanides & John C. Williams, 2003. "Inflation scares and forecast-based monetary policy," Working Paper Series 2003-11, Federal Reserve Bank of San Francisco.
  8. James B. Bullard & In-Koo Cho, 2003. "Escapist policy rules," Working Papers 2002-002, Federal Reserve Bank of St. Louis.
  9. 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.
  10. Milani, Fabio, 2008. "Learning, monetary policy rules, and macroeconomic stability," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3148-3165, October.
  11. Campbell, John, 1994. "Inspecting the Mechanism: An Analytical Approach to the Stochastic Growth Model," Scholarly Articles 3196342, Harvard University Department of Economics.
  12. 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.
  13. 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.
  14. Marcet, Albert & Nicolini, Juan Pablo, 1998. "Recurrent Hyperinflations and Learning," CEPR Discussion Papers 1875, C.E.P.R. Discussion Papers.
  15. 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.
  16. Fabio Milani, 2005. "Expectations, Learning and Macroeconomic Persistence," Working Papers 050608, University of California-Irvine, Department of Economics.
  17. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Macroeconomics 0506013, EconWPA.
  18. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April.
  19. Cho, In-Koo & Sargent, Thomas J., 2000. "Escaping Nash inflation," Working Paper Series 0023, European Central Bank.
  20. 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.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:sce:scecf4:271. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.