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An ARFIMA multi-level model of dual-component expectations in repeated cross-sectional survey data

Author

Listed:
  • Steven D. Silver

    (California State University)

  • Marko Raseta

    (Keele University)

Abstract

Expectations for price in financial markets continue to be extensively investigated in multi-component models. An empirical assessment of the components of these models is challenged by the form of measured expectations in single components and sampling in repeated cross-sectional designs. We report an operationalization of a multi-component model of expectations in cross-sectional and time series data that are estimated in an ARFIMA multi-level model. Our results indicate the significance of measures of components we define at both agent and aggregate levels in predicting a widely cited measure of consumer expectations.

Suggested Citation

  • Steven D. Silver & Marko Raseta, 2021. "An ARFIMA multi-level model of dual-component expectations in repeated cross-sectional survey data," Empirical Economics, Springer, vol. 60(2), pages 683-699, February.
    • Handle: RePEc:spr:empeco:v:60:y:2021:i:2:d:10.1007_s00181-019-01757-7
    DOI: 10.1007/s00181-019-01757-7
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    1. So, Beong Soo & Shin, Dong Wan, 1999. "Recursive mean adjustment in time-series inferences," Statistics & Probability Letters, Elsevier, vol. 43(1), pages 65-73, May.
    2. Hüsler, A. & Sornette, D. & Hommes, C.H., 2013. "Super-exponential bubbles in lab experiments: Evidence for anchoring over-optimistic expectations on price," Journal of Economic Behavior & Organization, Elsevier, vol. 92(C), pages 304-316.
    3. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
    4. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2015. "Markets, herding and response to external information," Papers 1506.03708, arXiv.org, revised Jun 2015.
    5. Thomas J. Sargent, 2013. "Rational Expectations and Inflation (Third Edition)," Economics Books, Princeton University Press, edition 3, volume 1, number 10000.
    6. Hommes, Cars & Sonnemans, Joep & Tuinstra, Jan & van de Velden, Henk, 2008. "Expectations and bubbles in asset pricing experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 67(1), pages 116-133, July.
    7. Robin Greenwood & Andrei Shleifer, 2014. "Expectations of Returns and Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 714-746.
    8. Graves, Timothy & Franzke, Christian L.E. & Watkins, Nicholas W. & Gramacy, Robert B. & Tindale, Elizabeth, 2017. "Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 60-71.
    9. Klinger, Sabine & Weber, Enzo, 2016. "Detecting unemployment hysteresis: A simultaneous unobserved components model with Markov switching," Economics Letters, Elsevier, vol. 144(C), pages 115-118.
    10. Daniel Kahneman, 2003. "Maps of Bounded Rationality: Psychology for Behavioral Economics," American Economic Review, American Economic Association, vol. 93(5), pages 1449-1475, December.
    11. W.-X. Zhou & D. Sornette, 2007. "Self-organizing Ising model of financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 175-181, January.
    12. Chen, Shiu-Sheng, 2011. "Lack of consumer confidence and stock returns," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 225-236, March.
    13. Schmeling, Maik & Schrimpf, Andreas, 2011. "Expected inflation, expected stock returns, and money illusion: What can we learn from survey expectations?," European Economic Review, Elsevier, vol. 55(5), pages 702-719, June.
    14. Mr. Sunil Sharma & Sushil Bikhchandani, 2000. "Herd Behavior in Financial Markets: A Review," IMF Working Papers 2000/048, International Monetary Fund.
    15. Griliches, Zvi & Ringstad, Vidar, 1970. "Error-in-the-Variables Bias in Nonlinear Contexts," Econometrica, Econometric Society, vol. 38(2), pages 368-370, March.
    16. Harras, Georges & Sornette, Didier, 2011. "How to grow a bubble: A model of myopic adapting agents," Journal of Economic Behavior & Organization, Elsevier, vol. 80(1), pages 137-152.
    17. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    18. Cont, Rama & Bouchaud, Jean-Philipe, 2000. "Herd Behavior And Aggregate Fluctuations In Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 4(2), pages 170-196, June.
    19. Wonho Song & Doojin Ryu & Robert I. Webb, 2018. "Volatility dynamics under an endogenous Markov-switching framework: a cross-market approach," Quantitative Finance, Taylor & Francis Journals, vol. 18(9), pages 1559-1571, September.
    20. Maria Andersson & Martin Hedesström & Tommy Gärling, 2014. "A Social-Psychological Perspective on Herding in Stock Markets," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 15(3), pages 226-234, July.
    21. Esam Mahdi & A. Ian McLeod, 2012. "Improved multivariate portmanteau test," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(2), pages 211-222, March.
    22. B. M. Roehner & D. Sornette, 2000. ""Thermometers" of Speculative Frenzy," Papers cond-mat/0001353, arXiv.org.
    23. Eckrot, A. & Jurczyk, J. & Morgenstern, I., 2016. "Ising model of financial markets with many assets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 250-254.
    24. Adrián Carro & Raúl Toral & Maxi San Miguel, 2015. "Markets, Herding and Response to External Information," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-28, July.
    25. Jean-Paul Fox & Cees Glas, 2003. "Bayesian modeling of measurement error in predictor variables using item response theory," Psychometrika, Springer;The Psychometric Society, vol. 68(2), pages 169-191, June.
    26. Matthew J. Lebo & Christopher Weber, 2015. "An Effective Approach to the Repeated Cross‐Sectional Design," American Journal of Political Science, John Wiley & Sons, vol. 59(1), pages 242-258, January.
    27. Arrow, Kenneth J, 1986. "Rationality of Self and Others in an Economic System," The Journal of Business, University of Chicago Press, vol. 59(4), pages 385-399, October.
    28. van Raaij, W. Fred, 1989. "Economic news, expectations and macro-economic behaviour," Journal of Economic Psychology, Elsevier, vol. 10(4), pages 473-493.
    29. Sarah Gelper & Roland Fried & Christophe Croux, 2010. "Robust forecasting with exponential and Holt-Winters smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 285-300.
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    More about this item

    Keywords

    Expectations; Multi-component models; Estimation in RCSs; Behavioral finance;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
    • G40 - Financial Economics - - Behavioral Finance - - - General

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