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

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  • 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
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    References listed on IDEAS

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    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. Edward J. Green & Richard M. Todd, 2001. "Thoughts on the Fed's role in the payments system," Annual Report, Federal Reserve Bank of Minneapolis, issue Apr, pages 6-27.
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    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. Leduc, Sylvain & Moran, Kevin & Vigfusson, Robert J., 2016. "Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications," International Finance Discussion Papers 1179, Board of Governors of the Federal Reserve System (U.S.).
    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. Di Pace, Frederico & Mitra, Kaushik & Zhang, Shoujian, 2016. "Adaptive learning and labour market dynamics," Bank of England working papers 633, Bank of England.
    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. Federico di Pace & Kaushik Mitra & Shoujian Zhang, 2014. "Adaptive Learning and Labour Market Dynamics," CDMA Working Paper Series 201408, Centre for Dynamic Macroeconomic Analysis.
    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. Frame, W. Scott & Gerardi, Kristopher S. & Willen, Paul S., 2015. "The failure of supervisory stress testing: Fannie Mae, Freddie Mac, and OFHEO," FRB Atlanta Working Paper 2015-3, Federal Reserve Bank of Atlanta.
    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, 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, pages 95-117.
    22. Leduc, Sylvain & Moran, Kevin & Vigfusson, Robert J., 2016. "Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications," International Finance Discussion Papers 1179, Board of Governors of the Federal Reserve System (U.S.).
    23. 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).
    24. 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

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