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. 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. Erceg, Christopher J. & Levin, Andrew T., 2003. "Imperfect credibility and inflation persistence," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 915-944, May.
    3. 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.
    4. Del Negro, Marco & Eusepi, Stefano, 2011. "Fitting observed inflation expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2105-2131.
    5. 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.
    6. N. Gregory Mankiw & Ricardo Reis & Justin Wolfers, 2004. "Disagreement about Inflation Expectations," NBER Chapters, in: NBER Macroeconomics Annual 2003, Volume 18, pages 209-270, National Bureau of Economic Research, Inc.
    7. Orphanides, Athanasios & Williams, John C., 2005. "The decline of activist stabilization policy: Natural rate misperceptions, learning, and expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1927-1950, November.
    8. Albert Marcet & Juan P. Nicolini, 2003. "Recurrent Hyperinflations and Learning," American Economic Review, American Economic Association, vol. 93(5), pages 1476-1498, December.
    9. Blanchard, Olivier Jean & Kahn, Charles M, 1980. "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, vol. 48(5), pages 1305-1311, July.
    10. Thomas Sargent & Noah Williams & Tao Zha, 2006. "Shocks and Government Beliefs: The Rise and Fall of American Inflation," American Economic Review, American Economic Association, vol. 96(4), pages 1193-1224, September.
    11. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    12. Fabio Milani, 2011. "Expectation Shocks and Learning as Drivers of the Business Cycle," Economic Journal, Royal Economic Society, vol. 121(552), pages 379-401, May.
    13. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    14. Binder, Michael & Pesaran, M. Hashem, 1997. "Multivariate Linear Rational Expectations Models," Econometric Theory, Cambridge University Press, vol. 13(6), pages 877-888, December.
    15. Branch, William A. & Evans, George W., 2006. "A simple recursive forecasting model," Economics Letters, Elsevier, vol. 91(2), pages 158-166, May.
    16. George W. Evans, 2001. "Expectations in Macroeconomics. Adaptive versus Eductive Learning," Revue Économique, Programme National Persée, vol. 52(3), pages 573-582.
    17. Fabio Canova & Luca Gambetti, 2010. "Do Expectations Matter? The Great Moderation Revisited," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(3), pages 183-205, July.
    18. 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.
    19. 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.
    20. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
    21. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
    22. Marimon, Ramon & Scott, Andrew (ed.), 1999. "Computational Methods for the Study of Dynamic Economies," OUP Catalogue, Oxford University Press, number 9780198294979.
    23. Christiano, Lawrence J, 2002. "Solving Dynamic Equilibrium Models by a Method of Undetermined Coefficients," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 21-55, October.
    24. Bray, Margaret, 1982. "Learning, estimation, and the stability of rational expectations," Journal of Economic Theory, Elsevier, vol. 26(2), pages 318-339, April.
    25. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
    26. Carboni, Giacomo & Ellison, Martin, 2009. "The Great Inflation and the Greenbook," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 831-841, September.
    27. Peter N. Ireland, 2003. "Irrational expectations and econometric practice: discussion of Orphanides and Williams, \"Inflation scares and forecast-based monetary policy\"," FRB Atlanta Working Paper 2003-22, Federal Reserve Bank of Atlanta.
    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. Sylvain Leduc & Kevin Moran & Robert J. Vigfusson, 2023. "Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications," The Review of Economics and Statistics, MIT Press, vol. 105(2), pages 392-407, March.
    2. Kuang, Pei & Mitra, Kaushik, 2016. "Long-run growth uncertainty," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 67-80.
    3. 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.
    4. Iliopulos, Eleni & Perego, Erica & Sopraseuth, Thepthida, 2021. "International business cycles: Information matters," Journal of Monetary Economics, Elsevier, vol. 123(C), pages 19-34.
    5. Audzei, Volha, 2023. "Learning and cross-country correlations in a multi-country DSGE model," Economic Modelling, Elsevier, vol. 120(C).
    6. 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.
    7. 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.
    8. Audzei, Volha & Slobodyan, Sergey, 2022. "Sparse restricted perceptions equilibrium," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    9. 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.
    10. Vázquez, Jesús & Aguilar, Pablo, 2021. "Adaptive learning with term structure information," European Economic Review, Elsevier, vol. 134(C).
    11. 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.
    12. 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.
    13. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arturo Ormeño, 2011. "Using Survey Data on Inflation Expectations in the Estimation of Learning and Rational Expectations Models," CESifo Working Paper Series 3552, CESifo.
    2. Ormeño, Arturo, 2012. "Using Survey Data on Inflation Expectations in the Estimation of Learning and Rational Expectations Models," Working Papers 2012-007, Banco Central de Reserva del Perú.
    3. Stephen J. Cole, 2020. "The Limits of Central Bank forward Guidance under Learning," International Journal of Central Banking, International Journal of Central Banking, vol. 16(4), pages 199-250, September.
    4. Cars Hommes & Kostas Mavromatis & Tolga Özden & Mei Zhu, 2023. "Behavioral learning equilibria in New Keynesian models," Quantitative Economics, Econometric Society, vol. 14(4), pages 1401-1445, November.
    5. Berardi, Michele & Galimberti, Jaqueson K., 2017. "Empirical calibration of adaptive learning," Journal of Economic Behavior & Organization, Elsevier, vol. 144(C), pages 219-237.
    6. 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.
    7. George W. Evans & Seppo Honkapohja, 2009. "Expectations, Learning and Monetary Policy: An Overview of Recent Research," Central Banking, Analysis, and Economic Policies Book Series, in: Klaus Schmidt-Hebbel & Carl E. Walsh & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series (ed.),Monetary Policy under Uncertainty and Learning, edition 1, volume 13, chapter 2, pages 027-076, Central Bank of Chile.
    8. Molnár, Krisztina & Santoro, Sergio, 2014. "Optimal monetary policy when agents are learning," European Economic Review, Elsevier, vol. 66(C), pages 39-62.
    9. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
    10. Stephen J. Cole, 2021. "Learning and the Effectiveness of Central Bank Forward Guidance," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(1), pages 157-200, February.
    11. Del Negro, Marco & Eusepi, Stefano, 2011. "Fitting observed inflation expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2105-2131.
    12. 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.
    13. Beqiraj Elton & Di Bartolomeo Giovanni & Serpieri Carolina, 2017. "Bounded-rationality and heterogeneous agents: Long or short forecasters?," wp.comunite 00132, Department of Communication, University of Teramo.
    14. Elton Beqiraj & Giovanni Di Bartolomeo & Marco Di Pietro & Carolina Serpieri, 2020. "Bounded rationality and heterogeneous expectations: Euler versus anticipated-utility approach," Journal of Economics, Springer, vol. 130(3), pages 249-273, August.
    15. Best, Gabriela, 2017. "Policy Preferences And Policy Makers' Beliefs: The Great Inflation," Macroeconomic Dynamics, Cambridge University Press, vol. 21(8), pages 1957-1995, December.
    16. Quaghebeur, Ewoud, 2019. "Learning And The Size Of The Government Spending Multiplier," Macroeconomic Dynamics, Cambridge University Press, vol. 23(8), pages 3189-3224, December.
    17. George W. Evans & Seppo Honkapohja, 2009. "Expectations, Learning and Monetary Policy: An Overview of Recent Research," Central Banking, Analysis, and Economic Policies Book Series, in: Klaus Schmidt-Hebbel & Carl E. Walsh & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series (ed.),Monetary Policy under Uncertainty and Learning, edition 1, volume 13, chapter 2, pages 027-076, Central Bank of Chile.
    18. Michele Berardi & Jaqueson K. Galimberti, 2012. "On the plausibility of adaptive learning in macroeconomics: A puzzling conflict in the choice of the representative algorithm," Centre for Growth and Business Cycle Research Discussion Paper Series 177, Economics, The University of Manchester.
    19. Farkas, Mátyás & Tatar, Balint, 2020. "Bayesian estimation of DSGE models with Hamiltonian Monte Carlo," IMFS Working Paper Series 144, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    20. Gelfer, Sacha, 2020. "The effects of professional forecast dissemination on macroeconomic volatility," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 131-156.

    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

    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.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Karen Reed-Larsen (email available below). General contact details of provider: https://edirc.repec.org/data/sonhhno.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.