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Near-Rational Expectations: How Far are Surveys from Rationality?

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  • Sergey Ivashchenko
  • Rangan Gupta

Abstract

New simple forms of deviation from rational expectations (RE) are suggested: temporary near-rational expectations (TNRE) and persistent near-rational expectations (PNRE). The medium-scale DSGE model was estimated with the RE, the TNRE and the PNRE. It was estimated with and without observations from the survey's expectations. The quality of the out-of-sample forecasts was estimated. It is shown that near-rational concepts produce the same advantages as learning, without its disadvantages (including the absence of ‘learning expectations’ reactions on policy change). The influence of the observed expectations on forecasting quality was analysed.

Suggested Citation

  • Sergey Ivashchenko & Rangan Gupta, 2017. "Near-Rational Expectations: How Far are Surveys from Rationality?," EERI Research Paper Series EERI RP 2017/04, Economics and Econometrics Research Institute (EERI), Brussels.
    • Handle: RePEc:eei:rpaper:eeri_rp_2017_04
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    References listed on IDEAS

    as
    1. Slobodyan, Sergey & Wouters, Raf, 2012. "Learning in an estimated medium-scale DSGE model," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 26-46.
    2. Arturo Ormeño & Krisztina Molnár, 2015. "Using Survey Data of Inflation Expectations in the Estimation of Learning and Rational Expectations Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(4), pages 673-699, June.
    3. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, Elsevier.
    4. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
    5. Fabio Milani, 2012. "The Modeling of Expectations in Empirical DSGE Models: a Survey," Working Papers 121301, University of California-Irvine, Department of Economics.
    6. Lombardo, Giovanni & Vestin, David, 2008. "Welfare implications of Calvo vs. Rotemberg-pricing assumptions," Economics Letters, Elsevier, vol. 100(2), pages 275-279, August.
    7. Fabio Milani & Ashish Rajbhandari, 2012. "Expectation Formation and Monetary DSGE Models: Beyond the Rational Expectations Paradigm," Working Papers 111212, University of California-Irvine, Department of Economics.
    8. Sergey Ivashchenko, 2014. "Forecasting in a Non-Linear DSGE Model," EUSP Department of Economics Working Paper Series Ec-02/14, European University at St. Petersburg, Department of Economics.
    9. Rubaszek, Michal & Skrzypczynski, Pawel, 2008. "On the forecasting performance of a small-scale DSGE model," International Journal of Forecasting, Elsevier, vol. 24(3), pages 498-512.
    10. Rochelle M. Edge & Refet S. Gurkaynak, 2010. "How Useful Are Estimated DSGE Model Forecasts for Central Bankers?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(2 (Fall)), pages 209-259.
    11. Hall, Jamie, 2012. "Consumption dynamics in general equilibrium," MPRA Paper 43933, University Library of Munich, Germany.
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    More about this item

    Keywords

    DSGE; out-of-sample forecasts; survey expectations; near-rational expectations.;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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