IDEAS home Printed from https://ideas.repec.org/p/edn/sirdps/314.html
   My bibliography  Save this paper

Baysian Model Averaging, Learning and Model Selection

Author

Listed:
  • Mitra, Kaushik
  • Evans, George W.
  • Honkapohja, Seppo

Abstract

Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.

Suggested Citation

  • Mitra, Kaushik & Evans, George W. & Honkapohja, Seppo, 2012. "Baysian Model Averaging, Learning and Model Selection," SIRE Discussion Papers 2012-11, Scottish Institute for Research in Economics (SIRE).
  • Handle: RePEc:edn:sirdps:314
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10943/314
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Thomas J. Sargent & Noah Williams, 2005. "Impacts of Priors on Convergence and Escapes from Nash Inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 360-391, April.
    2. Timothy Cogley & Thomas J. Sargent, 2005. "The conquest of US inflation: Learning and robustness to model uncertainty," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 528-563, April.
    3. In-Koo Cho & Noah Williams & Thomas J. Sargent, 2002. "Escaping Nash Inflation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(1), pages 1-40.
    4. Young, H. Peyton, 2004. "Strategic Learning and its Limits," OUP Catalogue, Oxford University Press, number 9780199269181, Decembrie.
    5. George W. Evans & Seppo Honkapohja, 2009. "Learning and Macroeconomics," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 421-451, May.
    6. McGough, Bruce, 2003. "Statistical Learning With Time-Varying Parameters," Macroeconomic Dynamics, Cambridge University Press, vol. 7(1), pages 119-139, February.
    7. Bullard, James, 1992. "Time-varying parameters and nonconvergence to rational expectations under least squares learning," Economics Letters, Elsevier, vol. 40(2), pages 159-166, October.
    8. Bray, Margaret M & Savin, Nathan E, 1986. "Rational Expectations Equilibria, Learning, and Model Specification," Econometrica, Econometric Society, vol. 54(5), pages 1129-1160, 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. Audzei, Volha, 2023. "Learning and cross-country correlations in a multi-country DSGE model," Economic Modelling, Elsevier, vol. 120(C).
    2. Carlos Carvalho & Stefano Eusepi & Emanuel Moench & Bruce Preston, 2023. "Anchored Inflation Expectations," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(1), pages 1-47, January.
    3. Fabrizio Coricelli & Zorobabel Bicaba, 2015. "Learning to open up: Capital account liberalizations in the post-Bretton Woods era," Working Papers halshs-01267264, HAL.
    4. Tortorice, Daniel L, 2018. "The business cycle implications of fluctuating long run expectations," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 266-291.
    5. Vidakovic, Neven, 2014. "Exchange rate regime and household's choice of debt," MPRA Paper 54219, University Library of Munich, Germany.

    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. Berardi, Michele & Galimberti, Jaqueson K., 2017. "Empirical calibration of adaptive learning," Journal of Economic Behavior & Organization, Elsevier, vol. 144(C), pages 219-237.
    2. In-Koo Cho & Kenneth Kasa, 2015. "Learning and Model Validation," Review of Economic Studies, Oxford University Press, vol. 82(1), pages 45-82.
    3. Bullard, James & Suda, Jacek, 2016. "The stability of macroeconomic systems with Bayesian learners," Journal of Economic Dynamics and Control, Elsevier, vol. 62(C), pages 1-16.
    4. In-Koo Cho & Kenneth Kasa, 2017. "Gresham's Law of Model Averaging," American Economic Review, American Economic Association, vol. 107(11), pages 3589-3616, November.
    5. Norman, Thomas W.L., 2015. "Learning, hypothesis testing, and rational-expectations equilibrium," Games and Economic Behavior, Elsevier, vol. 90(C), pages 93-105.
    6. Berardi, Michele & Galimberti, Jaqueson K., 2013. "A note on exact correspondences between adaptive learning algorithms and the Kalman filter," Economics Letters, Elsevier, vol. 118(1), pages 139-142.
    7. Thomas Sargent & Noah Williams & Tao Zha, 2006. "Shocks and Government Beliefs: The Rise and Fall of American Inflation," American Economic Review, American Economic Association, vol. 96(4), pages 1193-1224, September.
    8. Mitra, Kaushik & Evans, George W. & Honkapohja, Seppo, 2012. "Baysian Model Averaging, Learning and Model Selection," SIRE Discussion Papers 2012-11, Scottish Institute for Research in Economics (SIRE).
    9. George W. Evans & Seppo Honkapohja & Kaushik Mitra, 2022. "Expectations, Stagnation, And Fiscal Policy: A Nonlinear Analysis," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(3), pages 1397-1425, August.
    10. William Branch & George W. Evans, 2007. "Model Uncertainty and Endogenous Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(2), pages 207-237, April.
    11. Evans, David & Evans, George W. & McGough, Bruce, 2022. "The RPEs of RBCs and other DSGEs," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    12. Kolyuzhnov, Dmitri & Bogomolova, Anna & Slobodyan, Sergey, 2014. "Escape dynamics: A continuous-time approximation," Journal of Economic Dynamics and Control, Elsevier, vol. 38(C), pages 161-183.
    13. Ellison, Martin & Carboni, Giacomo, 2007. "Learning and the Great Inflation," CEPR Discussion Papers 6250, C.E.P.R. Discussion Papers.
    14. Seppo Honkapohja & Kaushik Mitra, 2006. "Learning Stability in Economies with Heterogeneous Agents," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 9(2), pages 284-309, April.
    15. J. Huston McCulloch, 2005. "The Kalman Foundations of Adaptive Least Squares: Applications to Unemployment and Inflation," Computing in Economics and Finance 2005 239, Society for Computational Economics.
    16. Dmitri Kolyuzhnov & Anna Bogomolova, 2004. "Escape Dynamics: A Continuous Time Approximation," Econometric Society 2004 Latin American Meetings 27, Econometric Society.
    17. George W. Evans & Seppo Honkapohja & Kaushik Mitra, 2012. "Does Ricardian Equivalence Hold When Expectations Are Not Rational?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(7), pages 1259-1283, October.
    18. Julian Kozlowski & Laura Veldkamp & Venky Venkateswaran, 2019. "The Tail That Keeps the Riskless Rate Low," NBER Macroeconomics Annual, University of Chicago Press, vol. 33(1), pages 253-283.
    19. Julian Kozlowski & Laura Veldkamp & Venky Venkateswaran, 2020. "The Tail That Wags the Economy: Beliefs and Persistent Stagnation," Journal of Political Economy, University of Chicago Press, vol. 128(8), pages 2839-2879.
    20. Pierpaolo Battigalli & Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Thomas Sargent, 2016. "A Framework for the Analysis of Self-Confirming Policies," Working Papers 573, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    More about this item

    Keywords

    Learning dynamics; Bayesian model averaging; grain of truth; self-referential systems;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    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:edn:sirdps:314. 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: Research Office (email available below). General contact details of provider: https://edirc.repec.org/data/sireeuk.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.