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Survey Evidence on the Rationality of Expectations

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  • Ivaldi, Marc

Abstract

Data on expectations collected by business surveys offer the opportunity to test the rational expectation hypothesis. Tests directly based on qualitative data are not powerful because of the nature of the data. An alternative method is proposed in this paper. The author applies the usual orthogonality conditions of rationality to the unobservable variables which are assumed to trigger the categorical responses of surveys when they cross certain thresholds. To implement the test, an original estimation method for latent variable models using indicators is required. The results show that the Hypothesis is not always rejected for the expectations of changes in demand drawn from surveys of French manufacturing industry. Copyright 1992 by John Wiley & Sons, Ltd.

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  • Ivaldi, Marc, 1992. "Survey Evidence on the Rationality of Expectations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(3), pages 225-241, July-Sept.
    • Handle: RePEc:jae:japmet:v:7:y:1992:i:3:p:225-41
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    Cited by:

    1. Lui, Silvia & Mitchell, James & Weale, Martin, 2011. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1128-1146, October.
    2. Pierre‐Daniel Sarte, 2014. "When Is Sticky Information More Information?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1345-1379, October.
    3. Silva Lopes, Artur, 1994. "A "hipótese das expectativas racionais": teoria e realidade (uma visita guiada à literatura até 1992) [The "rational expectations hypothesis": theory and reality (a guided tour ," MPRA Paper 9699, University Library of Munich, Germany, revised 23 Jul 2008.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    6. Xavier D'Haultfoeuille & Christophe Gaillac & Arnaud Maurel, 2021. "Rationalizing rational expectations: Characterizations and tests," Quantitative Economics, Econometric Society, vol. 12(3), pages 817-842, July.
    7. Yu, Ge, 2003. "Comparing Expectations and Outcomes: Application to UK Data," MPRA Paper 502, University Library of Munich, Germany, revised 2005.
    8. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    9. Das, J.W.M. & van Soest, A.H.O., 2000. "Expected Versus Realized Income Changes : A Test of the Rational Expectation Hypothesis," Discussion Paper 2000-105, Tilburg University, Center for Economic Research.
    10. Christian Mueller & Aniela Wirz & Nora Sydow, 2007. "A Note on the Carlson-Parkin Method of Quantifying Qualitative Data," KOF Working papers 07-168, KOF Swiss Economic Institute, ETH Zurich.
    11. Wärneryd, K.E., 1995. "Demystifying rational expectations theory through an economic-psychological model," Discussion Paper 1995-92, Tilburg University, Center for Economic Research.
    12. Mitchell, James & Weale, Martin R., 2007. "The rationality and reliability of expectations reported by British households: micro evidence from the British household panel survey," Discussion Paper Series 1: Economic Studies 2007,19, Deutsche Bundesbank.
    13. Catherine Doz, 1993. "Note sur les tests de rationalité des prévisions," Économie et Prévision, Programme National Persée, vol. 108(2), pages 129-133.
    14. Pierre-Daniel G. Sarte, 2010. "Learning about informational rigidities from sectoral data and diffusion indices," Working Paper 10-09, Federal Reserve Bank of Richmond.
    15. Michela Nardo, 2003. "The Quantification of Qualitative Survey Data: A Critical Assessment," Journal of Economic Surveys, Wiley Blackwell, vol. 17(5), pages 645-668, December.
    16. Christian Mueller & Eva M. Koeberl, 2008. "Business Cycle Measurement with Semantic Filtering: A Micro Data Approach," KOF Working papers 08-212, KOF Swiss Economic Institute, ETH Zurich.
    17. Santiago Pinto & Pierre-Daniel Sarte & Robert Sharp, 2020. "The Information Content and Statistical Properties of Diffusion Indexes," International Journal of Central Banking, International Journal of Central Banking, vol. 16(4), pages 47-99, September.

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