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Are the representative agent’s beliefs based on efficient econometric models?

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  • Bovi, Maurizio

Abstract

No, they are not; at least not in the UK. By examining GDP dynamics we find that, over a time-span of two decades, an easy-to-perform adaptive expectations model systematically outperforms other standard predictors in terms of squared forecasting errors. This should reduce model uncertainty and thereby lead to increased homogeneity in expectations. However, data collected in surveys show that great variety in expectations persists even in this situation. Moreover, Granger tests indicate that the forecasting fitness of the best predictor can be further enhanced by the use of information provided by survey expectations. These results, based on real-time data and robust to both several predictors and nonlinearities, weaken the general validity of approaches assuming predictions based on efficient econometric models.

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  • Bovi, Maurizio, 2013. "Are the representative agent’s beliefs based on efficient econometric models?," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 633-648.
  • Handle: RePEc:eee:dyncon:v:37:y:2013:i:3:p:633-648
    DOI: 10.1016/j.jedc.2012.10.005
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    Cited by:

    1. 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.
    2. Gasteiger, Emanuel, 2018. "Do Heterogeneous Expectations Constitute A Challenge For Policy Interaction?," Macroeconomic Dynamics, Cambridge University Press, vol. 22(8), pages 2107-2140, December.
    3. Maurizio Bovi & Roy Cerqueti, 2016. "Forecasting macroeconomic fundamentals in economic crises," Annals of Operations Research, Springer, vol. 247(2), pages 451-469, December.
    4. Maurizio Bovi, 2014. "Shocks and the Expectations Formation Process. A Tale of Two Expectations," Natural Field Experiments 00390, The Field Experiments Website.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "Tracking economic growth by evolving expectations via genetic programming: A two-step approach," Working Papers XREAP2018-4, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2018.
    6. Nakagawa, Ryuichi, 2015. "Learnability of an equilibrium with private information," Journal of Economic Dynamics and Control, Elsevier, vol. 59(C), pages 58-74.
    7. Maurizio Bovi, 2016. "The tale of two expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(6), pages 2677-2705, November.
    8. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    9. Maurizio Bovi, 2020. "A time-varying expectations formation mechanism," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(1), pages 69-103, April.
    10. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    11. Maurizio Bovi & Massimo Mancini, 2016. "Recessions, expectations, and labor supply dynamics," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(2), pages 653-671, March.

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    More about this item

    Keywords

    Survey expectations; Heterogeneous expectations; Forecasting models; Bounded rationality;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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