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Using Survey Data of Inflation Expectations in the Estimation of Learning and Rational Expectations Models

Author

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  • Ormeno, Arturo

    (Credit Suisse AG)

  • Molnar, Krisztina

    (Dept. of Economics, Norwegian School of Economics and Business Administration)

Abstract

Does survey data contain useful information for estimating macroeconomic models? We address this question by using survey data of inflation expectations to estimate the New Keynesian model by Smets and Wouters (2007) and compare its performance under rational expectations and adaptive learning. The survey information serves as an additional moment restriction and helps us to determine the learning agents' forecasting model for in ation. Adaptive learning fares similarly to rational expectations in fitting macro data, but clearly outperforms rational expectations in fitting macro and survey data simultaneously. In other words survey data contains additional information that is not present in the macro data alone.

Suggested Citation

  • Ormeno, Arturo & Molnar, Krisztina, 2014. "Using Survey Data of Inflation Expectations in the Estimation of Learning and Rational Expectations Models," Discussion Paper Series in Economics 20/2014, Norwegian School of Economics, Department of Economics.
  • Handle: RePEc:hhs:nhheco:2014_020
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    References listed on IDEAS

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    Cited by:

    1. Eleni Iliopulos & Erica Perego & Thepthida Sopraseuth, 2018. "International business cycles: Information matters," THEMA Working Papers 2018-13, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    2. Mele, Antonio & Molnár, Krisztina & Santoro, Sergio, 2020. "On the perils of stabilizing prices when agents are learning," Journal of Monetary Economics, Elsevier, vol. 115(C), pages 339-353.
    3. Roberta Cardani & Alessia Paccagnini & Stelios D. Bekiros, 2017. "The Effectiveness of Forward Guidance in an Estimated DSGE Model for the Euro Area: the Role of Expectations," Working Papers 201701, School of Economics, University College Dublin.
    4. F. Di Pace & K. Mitra & S. Zhang, 2021. "Adaptive Learning and Labor Market Dynamics," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(2-3), pages 441-475, March.
    5. Sergey Ivashchenko & Rangan Gupta, 2017. "Near-Rational Expectations: How Far are Surveys from Rationality?," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 60(1), pages 1-27.
    6. Kortelainen, Mika & Paloviita, Maritta & Viren, Matti, 2016. "How useful are measured expectations in estimation and simulation of a conventional small New Keynesian macro model?," Economic Modelling, Elsevier, vol. 52(PB), pages 540-550.
    7. Volha Audzei & Sergey Slobodyan, 2018. "Sparse Restricted Perception Equilibrium," Working Papers 2018/8, Czech National Bank.
    8. Sylvain Leduc & Kevin Moran & Robert J. Vigfusson, 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.).
    9. Vázquez, Jesús & Aguilar, Pablo, 2021. "Adaptive learning with term structure information," European Economic Review, Elsevier, vol. 134(C).
    10. Kuang, Pei & Mitra, Kaushik, 2016. "Long-run growth uncertainty," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 67-80.
    11. Sergey Ivashchenko & Rangan Gupta, 2017. "Near-Rational Expectations: How Far are Surveys from Rationality?," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 60(1), pages 1-27.
    12. Gobbi, Lucio & Mazzocchi, Ronny & Tamborini, Roberto, 2019. "Monetary policy, de-anchoring of inflation expectations, and the “new normal”," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.

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    More about this item

    Keywords

    Survey data; learning; rational expectations; inflation expectations; Bayesian econometrics.;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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