IDEAS home Printed from https://ideas.repec.org/p/urs/urswps/12-01.html
   My bibliography  Save this paper

Estimation of Constant Gain Learning Models

Author

Abstract

This paper provides a concise primer on the estimation of constant gain learning models. One practical concern in the estimation procedure is the initialization of the learning parameters. The popular approach in the literature relies on a training sample to estimate these quantities. We also consider the alternative, that of estimating these alongside the other model parameters. As we show with the aid of both simulated data and real data examples, the estimates are comparable using either approach.

Suggested Citation

  • Eric Gaus & Srikanth Ramamurthy, 2012. "Estimation of Constant Gain Learning Models," Working Papers 12-01, Ursinus College, Department of Economics, revised 01 Apr 2014.
  • Handle: RePEc:urs:urswps:12-01
    as

    Download full text from publisher

    File URL: http://webpages.ursinus.edu/egaus/Research/CGL_GR.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Slobodyan, Sergey & Wouters, Raf, 2012. "Learning in an estimated medium-scale DSGE model," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 26-46.
    2. Chevillon, Guillaume & Massmann, Michael & Mavroeidis, Sophocles, 2010. "Inference in models with adaptive learning," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 341-351, April.
    3. Bruce Preston, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Berardi, Michele & Galimberti, Jaqueson K., 2017. "On the initialization of adaptive learning in macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, vol. 78(C), pages 26-53.

    More about this item

    Keywords

    Adaptive Learning; Rational Expectations; MCMC; Bayesian Econometrics;

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    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:urs:urswps:12-01. 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: (Eric Gaus). General contact details of provider: http://edirc.repec.org/data/ebursus.html .

    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.