IDEAS home Printed from https://ideas.repec.org/a/aea/aejmac/v4y2012i2p65-101.html
   My bibliography  Save this article

Learning in a Medium-Scale DSGE Model with Expectations Based on Small Forecasting Models

Author

Listed:
  • Sergey Slobodyan
  • Raf Wouters

Abstract

This paper evaluates the empirical performance of a medium-scale DSGE model with agents forming expectations using small forecasting models updated by the Kalman filter. The adaptive learning model fits the data better than the rational expectations (RE) model. Beliefs about the inflation persistence explain the observed decline in the mean and the volatility of inflation as well as Phillips curve flattening. Learning about inflation results in lower estimates for the persistence of the exogenous shocks that drive price and wage dynamics in the RE version of the model. Expectations based on small forecasting models are closely related to the survey evidence on inflation expectations. (JEL C53, D83, D84, E13, E17, E31)

Suggested Citation

  • Sergey Slobodyan & Raf Wouters, 2012. "Learning in a Medium-Scale DSGE Model with Expectations Based on Small Forecasting Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 4(2), pages 65-101, April.
    • Handle: RePEc:aea:aejmac:v:4:y:2012:i:2:p:65-101
    Note: DOI: 10.1257/mac.4.2.65
    as

    Download full text from publisher

    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/mac.4.2.65
    Download Restriction: no
    File URL: http://www.aeaweb.org/aej/mac/data/2010-0212_data.zip
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.

    References listed on IDEAS

    as
    1. Claudio E. V. Borio & Andrew Filardo, 2007. "Globalisation and inflation: New cross-country evidence on the global determinants of domestic inflation," BIS Working Papers 227, Bank for International Settlements.
    2. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
    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. repec:eee:dyncon:v:78:y:2017:i:c:p:26-53 is not listed on IDEAS
    2. Cogley, Timothy & Matthes, Christian & Sbordone, Argia M., 2015. "Optimized Taylor rules for disinflation when agents are learning," Journal of Monetary Economics, Elsevier, vol. 72(C), pages 131-147.
    3. Sylvain Leduc & Kevin Moran & Robert J. Vigfusson, 2016. "Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications," CIRANO Working Papers 2016s-53, CIRANO.
    4. Benhabib, Jess & Evans, George W. & Honkapohja, Seppo, 2014. "Liquidity traps and expectation dynamics: Fiscal stimulus or fiscal austerity?," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 220-238.
    5. Federico di Pace & Kaushik Mitra & Shoujian Zhang, 2014. "Adaptive Learning and Labour Market Dynamics," CDMA Working Paper Series 201408, Centre for Dynamic Macroeconomic Analysis.
    6. Kuang, Pei & Mitra, Kaushik, 2016. "Long-run growth uncertainty," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 67-80.
    7. repec:eee:dyncon:v:82:y:2017:i:c:p:289-311 is not listed on IDEAS
    8. Pablo Aguilar & Jesús Vázquez, 2018. "Term structure and real-time learning," Working Papers 1803, Banco de España;Working Papers Homepage.
    9. Michael Hatcher & Patrick Minford, 2016. "Stabilisation Policy, Rational Expectations And Price-Level Versus Inflation Targeting: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 327-355, April.
    10. Hatcher, Michael & Minford, Patrick, 2014. "Stabilization policy, rational expectations and price-level versus infl‡ation targeting: a survey," CEPR Discussion Papers 9820, C.E.P.R. Discussion Papers.
    11. Norman, Thomas W.L., 2015. "Learning, hypothesis testing, and rational-expectations equilibrium," Games and Economic Behavior, Elsevier, vol. 90(C), pages 93-105.
    12. André, Marine Charlotte & Dai, Meixing, 2017. "Is central bank conservatism desirable under learning?," Economic Modelling, Elsevier, vol. 60(C), pages 281-296.
    13. Hommes, C.H. & Zhu, M., 2016. "Behavioral Learning Equilibria, Persistence Amplification & Monetary Policy," CeNDEF Working Papers 16-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    14. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    15. Fuhrer, Jeffrey C., 2015. "Expectations as a source of macroeconomic persistence: an exploration of firms' and households' expectation formation," Working Papers 15-5, Federal Reserve Bank of Boston.
    16. 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.
    17. Fuhrer, Jeff, 2017. "Expectations as a source of macroeconomic persistence: Evidence from survey expectations in a dynamic macro model," Journal of Monetary Economics, Elsevier, vol. 86(C), pages 22-35.
    18. Agustín Arias, 2016. "Sentiment Shocks as Drivers of Business Cycles," Working Papers Central Bank of Chile 782, Central Bank of Chile.
    19. Milani, Fabio, 2017. "Sentiment and the U.S. business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 289-311.
    20. Rychalovska, Yuliya, 2016. "The implications of financial frictions and imperfect knowledge in the estimated DSGE model of the U.S. economy," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 259-282.
    21. De Grauwe, Paul & Macchiarelli, Corrado, 2015. "Animal spirits and credit cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 59(C), pages 95-117.
    22. Pablo Aguilar & Jesús Vázquez, 2015. "The role of term structure in an estimated DSGE model with learning," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2015007, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    23. Dan Tortorice, 2016. "The Business Cycles Implications of Fluctuating Long Run Expectations," Working Papers 100, Brandeis University, Department of Economics and International Businesss School.

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

    Lists

    This item is featured on the following reading lists or Wikipedia pages:
    1. Learning in a Medium-Scale DSGE Model with Expectations Based on Small Forecasting Models (AEJ:MA 2012) in ReplicationWiki

    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:aea:aejmac:v:4:y:2012:i:2:p:65-101. 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: (Jane Voros) or (Michael P. Albert). General contact details of provider: http://edirc.repec.org/data/aeaaaea.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.