IDEAS home Printed from https://ideas.repec.org/p/sce/scecf3/280.html
   My bibliography  Save this paper

Adaptive Learning, Model Uncertainty and Monetary Policy Inertia in a Large Information Environment

Author

Listed:
  • Fabio Milani

Abstract

No abstract is available for this item.

Suggested Citation

  • Fabio Milani, 2003. "Adaptive Learning, Model Uncertainty and Monetary Policy Inertia in a Large Information Environment," Computing in Economics and Finance 2003 280, Society for Computational Economics.
    • Handle: RePEc:sce:scecf3:280
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below 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.

    Citations

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


    Cited by:

    1. William Goetzmann & Eduardas Valaitis, 2006. "Simulating Real Estate in the Investment Portfolio: Model Uncertainty and Inflation Hedging," Yale School of Management Working Papers amz2476, Yale School of Management, revised 01 May 2006.
    2. Fabio Milani, 2008. "Monetary Policy With A Wider Information Set: A Bayesian Model Averaging Approach," Scottish Journal of Political Economy, Scottish Economic Society, vol. 55(1), pages 1-30, February.
    3. Tondl, Gabriele & Prüfer, Patricia, 2007. "Does it Make a Difference? Comparing Growth Effects of European and North American FDI in Latin America," Proceedings of the German Development Economics Conference, Göttingen 2007 26, Verein für Socialpolitik, Research Committee Development Economics.

    More about this item

    Keywords

    optimal monetary policy; Bayesian Model Averaging; leading indicators; model uncertainty; adaptive learning; interest-rate smoothing; inertia.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

    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:sce:scecf3:280. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.