IDEAS home Printed from https://ideas.repec.org/a/bpj/sndecm/v17y2013i1p47-83n5.html
   My bibliography  Save this article

Learning under signal-to-noise ratio uncertainty

Author

Listed:
  • Ilek Alex

    (Research Division, Bank of Israel, Jerusalem 91007, Israel)

Abstract

The paper presents an alternative real time adaptive learning algorithm in the presence of signal-to-noise ratio uncertainty. The main innovation of this algorithm is that it uses a gain which is determined within the model: it continuously depends on the extent of misevaluation of parameters embedded in the forecast error. We show that in the presence of signal-to-noise ratio misevaluation, the usage of the proposed learning algorithm is a significant improvement on the Kalman Filter learning algorithm. In a full information case, the Kalman Filter learning algorithm is still the optimal tool.

Suggested Citation

  • Ilek Alex, 2013. "Learning under signal-to-noise ratio uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 47-83, February.
    • Handle: RePEc:bpj:sndecm:v:17:y:2013:i:1:p:47-83:n:5
    DOI: 10.1515/snde-2012-0046
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/snde-2012-0046
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/snde-2012-0046?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    2. Marcet, Albert & Sargent, Thomas J, 1989. "Convergence of Least-Squares Learning in Environments with Hidden State Variables and Private Information," Journal of Political Economy, University of Chicago Press, vol. 97(6), pages 1306-1322, December.
    3. Sims, Christopher A, 1980. "Comparison of Interwar and Postwar Business Cycles: Monetarism Reconsidered," American Economic Review, American Economic Association, vol. 70(2), pages 250-257, May.
    4. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    5. Christina D. Romer & David Romer, 2002. "The evolution of economic understanding and postwar stabilization policy," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 11-78.
    6. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    7. Chang-Jin Kim & Charles R. Nelson, 1999. "Has The U.S. Economy Become More Stable? A Bayesian Approach Based On A Markov-Switching Model Of The Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 608-616, November.
    8. Gabriel Perez-Quiros & Margaret M. McConnell, 2000. "Output Fluctuations in the United States: What Has Changed since the Early 1980's?," American Economic Review, American Economic Association, vol. 90(5), pages 1464-1476, December.
    9. Margaret M. McConnell & Gabriel Perez-Quiros, 2000. "Output fluctuations in the United States: what has changed since the early 1980s?," Proceedings, Federal Reserve Bank of San Francisco, issue mar.
    10. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    11. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Belomestny, Denis & Krymova, Ekaterina & Polbin, Andrey, 2021. "Bayesian TVP-VARX models with time invariant long-run multipliers," Economic Modelling, Elsevier, vol. 101(C).
    2. Denis Belomestny & Ekaterina Krymova & Andrey Polbin, 2020. "Estimating TVP-VAR models with time invariant long-run multipliers," Papers 2008.00718, arXiv.org.
    3. Pancrazi, Roberto, 2015. "The heterogeneous Great Moderation," European Economic Review, Elsevier, vol. 74(C), pages 207-228.
    4. Belongia, Michael T. & Ireland, Peter N., 2016. "The evolution of U.S. monetary policy: 2000–2007," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 78-93.
    5. Martínez-García Enrique, 2018. "Modeling time-variation over the business cycle (1960–2017): an international perspective," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-25, December.
    6. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    7. Chang‐Jin Kim & Pym Manopimoke & Charles R. Nelson, 2014. "Trend Inflation and the Nature of Structural Breaks in the New Keynesian Phillips Curve," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(2-3), pages 253-266, March.
    8. Chetan Dave & Marco M. Sorge, 2025. "Fat‐tailed DSGE models: A survey and new results," Journal of Economic Surveys, Wiley Blackwell, vol. 39(1), pages 146-171, February.
    9. Charles S. Bos & Siem Jan Koopman & Marius Ooms, 2007. "Long memory modelling of inflation with stochastic variance and structural breaks," CREATES Research Papers 2007-44, Department of Economics and Business Economics, Aarhus University.
    10. Nalan Basturk & Cem Cakmakli & Pinar Ceyhan & Herman K. van Dijk, 2013. "Posterior-Predictive Evidence on US Inflation using Phillips Curve Models with Non-Filtered Time Series," Tinbergen Institute Discussion Papers 13-011/III, Tinbergen Institute.
    11. Castelnuovo, Efrem, 2010. "Trend inflation and macroeconomic volatilities in the post-WWII U.S. economy," The North American Journal of Economics and Finance, Elsevier, vol. 21(1), pages 19-33, March.
    12. Ayse Kabukcuoglu & Enrique Martínez-García, 2016. "What Helps Forecast U.S. Inflation?—Mind the Gap!," Koç University-TUSIAD Economic Research Forum Working Papers 1615, Koc University-TUSIAD Economic Research Forum.
    13. Jinho Bae & Chang-Jin Kim & Dong Kim, 2012. "The evolution of the monetary policy regimes in the U.S," Empirical Economics, Springer, vol. 43(2), pages 617-649, October.
    14. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
    15. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    16. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, April.
    17. Steven J. Davis & James A. Kahn, 2008. "Interpreting the Great Moderation: Changes in the Volatility of Economic Activity at the Macro and Micro Levels," Journal of Economic Perspectives, American Economic Association, vol. 22(4), pages 155-180, Fall.
    18. Georgios P. Kouretas & Mark E. Wohar, 2012. "The dynamics of inflation: a study of a large number of countries," Applied Economics, Taylor & Francis Journals, vol. 44(16), pages 2001-2026, June.
    19. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    20. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:bpj:sndecm:v:17:y:2013:i:1:p:47-83:n:5. 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.

    If CitEc recognized a bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

    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.