IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/5627.html
   My bibliography  Save this paper

Adaptive Learning in Practice

Author

Listed:
  • Carceles-Poveda, Eva
  • Giannitsarou, Chryssi

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.

Suggested Citation

  • Carceles-Poveda, Eva & Giannitsarou, Chryssi, 2006. "Adaptive Learning in Practice," CEPR Discussion Papers 5627, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:5627
    as

    Download full text from publisher

    File URL: http://www.cepr.org/active/publications/discussion_papers/dp.php?dpno=5627
    Download Restriction: CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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, February.
    4. 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.
    5. 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.
    6. Bullard, James & Cho, In-Koo, 2005. "Escapist policy rules," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1841-1865, November.
    7. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Macroeconomics 0506013, EconWPA.
    8. Orphanides, Athanasios & Williams, John C., 2005. "The decline of activist stabilization policy: Natural rate misperceptions, learning, and expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1927-1950, November.
    9. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
    10. Albert Marcet & Juan P. Nicolini, 2003. "Recurrent Hyperinflations and Learning," American Economic Review, American Economic Association, vol. 93(5), pages 1476-1498, December.
    11. 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.
    12. Giannitsarou, Chryssi, 2006. "Supply-side reforms and learning dynamics," Journal of Monetary Economics, Elsevier, vol. 53(2), pages 291-309, March.
    13. 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.
    14. 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.
    15. 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, April.
    16. 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.
    17. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April.
    18. 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.
    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. Milani, Fabio, 2008. "Learning, monetary policy rules, and macroeconomic stability," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3148-3165, October.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    adaptive learning; computational methods; least square estimations; short-run dynamics;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cpr:ceprdp:5627. 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: (). General contact details of provider: .

    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 CitEc recognized a reference but did not link an item in RePEc 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.