IDEAS home Printed from https://ideas.repec.org/p/hhs/nhheco/2014_020.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://brage.bibsys.no/xmlui/bitstream/handle/11250/196296/1/workingpaper.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
    2. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
    3. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, vol. 105(8), pages 2644-2678, August.
    4. Branch, William A. & Evans, George W., 2006. "A simple recursive forecasting model," Economics Letters, Elsevier, vol. 91(2), pages 158-166, May.
    5. Carboni, Giacomo & Ellison, Martin, 2009. "The Great Inflation and the Greenbook," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 831-841, September.
    6. Raf Wouters & Sergey Slobodyan, 2009. "Estimating a medium–scale DSGE model with expectations based on small forecasting models," 2009 Meeting Papers 654, Society for Economic Dynamics.
    7. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
    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. 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.
    2. 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.
    3. Kuang, Pei & Mitra, Kaushik, 2016. "Long-run growth uncertainty," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 67-80.
    4. Federico di Pace & Kaushik Mitra & Shoujian Zhang, 2014. "Adaptive Learning and Labour Market Dynamics," CDMA Working Paper Series 201408, Centre for Dynamic Macroeconomic Analysis.
    5. Mele, Antonio & Molnar, Krisztina & Santoro, Sergio, 2014. "On the perils of stabilizing prices when agents are learning," Discussion Paper Series in Economics 1/2015, Norwegian School of Economics, Department of Economics.
    6. Sylvain Leduc & Kevin Moran & Robert J. Vigfusson, 2016. "Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications," CIRANO Working Papers 2016s-53, CIRANO.
    7. 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.
    8. Sergey Ivashchenko, 2014. "Near-Rational Expectations: How Far Are Surveys from Rationality?," EUSP Department of Economics Working Paper Series Ec-06/14, European University at St. Petersburg, Department of Economics.

    More about this item

    Keywords

    Survey data; learning; rational expectations; inflation expectations; Bayesian econometrics.;

    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

    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:hhs:nhheco:2014_020. 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: (Dagny Hanne Kristiansen). General contact details of provider: http://edirc.repec.org/data/sonhhno.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.