IDEAS home Printed from https://ideas.repec.org/p/red/sed007/954.html
   My bibliography  Save this paper

Adaptive Learning as a Propagation Mechanism

Author

Listed:
  • Bruce Preston

    (Columbia University)

  • Stefano Eusepi

    (Federal Reserve Bank of New York)

Abstract

Finally, following Smith (1993), we estimate the model using indirect inference methods. The empirical implications of the model both under learning and rational expectations are explored. Furthermore, we test the relative importance of various model frictions and learning dynamics in capturing the volatility and persistence of observed macroeconomic data.

Suggested Citation

  • Bruce Preston & Stefano Eusepi, 2007. "Adaptive Learning as a Propagation Mechanism," 2007 Meeting Papers 954, Society for Economic Dynamics.
    • Handle: RePEc:red:sed007:954
    as

    Download full text from publisher

    File URL: https://red-files-public.s3.amazonaws.com/meetpapers/2007/paper_954.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Cogley, Timothy & Nason, James M, 1995. "Output Dynamics in Real-Business-Cycle Models," American Economic Review, American Economic Association, vol. 85(3), pages 492-511, June.
    3. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 63-84, Suppl. De.
    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. Ireland, Peter N., 2004. "A method for taking models to the data," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1205-1226, March.
    2. Alexander Kriwoluzky, 2008. "Matching Theory and Data: Bayesian Vector Autoregression and Dynamic Stochastic General Equilibrium Models," SFB 649 Discussion Papers SFB649DP2008-060, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Adrian Pagan, 2001. "The Getting of Macroeconomic Wisdom," International Economic Association Series, in: Jacques Drèze (ed.), Advances in Macroeconomic Theory, chapter 11, pages 219-235, Palgrave Macmillan.
    4. Gáti, Laura, 2023. "Monetary policy & anchored expectations—An endogenous gain learning model," Journal of Monetary Economics, Elsevier, vol. 140(S), pages 37-47.
    5. Pablo A. Guerrón-Quintana & James M. Nason, 2013. "Bayesian estimation of DSGE models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 21, pages 486-512, Edward Elgar Publishing.
    6. Doshchyn, Artur & Giommetti, Nicola, 2013. "Learning, Expectations, and Endogenous Business Cycles," MPRA Paper 49617, University Library of Munich, Germany.
    7. Hall, Alastair R. & Inoue, Atsushi & Nason, James M. & Rossi, Barbara, 2012. "Information criteria for impulse response function matching estimation of DSGE models," Journal of Econometrics, Elsevier, vol. 170(2), pages 499-518.
    8. Smith, Gregor W. & Zin, Stanley E., 1997. "Real business-cycle realizations," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 47(1), pages 243-280, December.
    9. Norman Swanson & Oleg Korenok, 2006. "How Sticky Is Sticky Enough? A Distributional and Impulse Response Analysis of New Keynesian DSGE Models. Extended Working Paper Version," Departmental Working Papers 200612, Rutgers University, Department of Economics.
    10. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.
    11. Oleg Korenok & Norman R. Swanson, 2007. "How Sticky Is Sticky Enough? A Distributional and Impulse Response Analysis of New Keynesian DSGE Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(6), pages 1481-1508, September.
    12. Berkowitz, J. & Birgean, I. & Kilian, L., 1999. "On the Finite-Sample Accuracy of Nonparametric Resampling Algorithms for Economic Time Series," Papers 99-01, Michigan - Center for Research on Economic & Social Theory.
    13. Orphanides, Athanasios & Williams, John C., 2008. "Learning, expectations formation, and the pitfalls of optimal control monetary policy," Journal of Monetary Economics, Elsevier, vol. 55(Supplemen), pages 80-96, October.
    14. Chéron, Arnaud & Hairault, Jean-Olivier & Langot, François, 2004. "Labor Market Institutions and the Employment-Productivity Trade-Off: A Wage Posting Approach," IZA Discussion Papers 1364, Institute of Labor Economics (IZA).
    15. Blomberg, S. Brock & Hess, Gregory D., 2003. "Is the political business cycle for real?," Journal of Public Economics, Elsevier, vol. 87(5-6), pages 1091-1121, May.
    16. Peter Fuleky & Eric Zivot, 2014. "Indirect inference based on the score," Econometrics Journal, Royal Economic Society, vol. 17(3), pages 383-393, October.
    17. James E. Hartley, 1998. "A Skeptical Note on the Importance of Technology Shocks," The American Economist, Sage Publications, vol. 42(1), pages 108-111, March.
    18. Atanas Christev, 2006. "Learning Hyperinflations," Computing in Economics and Finance 2006 475, Society for Computational Economics.
    19. Yong Bao & Xiaotian Liu & Lihong Yang, 2020. "Indirect Inference Estimation of Spatial Autoregressions," Econometrics, MDPI, vol. 8(3), pages 1-26, September.
    20. Robert E. Hall, 2002. "Industry Dynamics with Adjustment Costs," NBER Working Papers 8849, National Bureau of Economic Research, Inc.

    More about this item

    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:red:sed007:954. 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: Christian Zimmermann (email available below). General contact details of provider: https://edirc.repec.org/data/sedddea.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.