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The importance of time series extrapolation for macroeconomic expectations

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  • Roos, Michael W.M.
  • Schmidt, Ulrich

Abstract

This paper presents a simple experiment on how laypeople form macroeconomic expectations. Subjects have to forecast inflation and GDP growth. By varying the information provided in different treatments, we can assess the importance of historical time-series information versus information acquired outside the experimental setting such as knowledge of expert forecasts. It turns out that the availability of historical data has a dominant impact on expectations and wipes out the influence of outside-lab information completely. Consequently, backward-looking behavior can be identified unambiguously as a decisive factor in expectation formation.

Suggested Citation

  • Roos, Michael W.M. & Schmidt, Ulrich, 2011. "The importance of time series extrapolation for macroeconomic expectations," Kiel Working Papers 1723, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkwp:1723
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    References listed on IDEAS

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    Cited by:

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    2. Paul De Grauwe & Eddie Gerba, 2017. "Monetary transmission under competing corporate finance regimes," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 35(82), pages 78-100, April.
    3. Jang, Tae-Seok & Sacht, Stephen, 2012. "Identification of animal spirits in a bounded rationality model: An application to the euro area," Economics Working Papers 2012-12, Christian-Albrechts-University of Kiel, Department of Economics.
    4. De Grauwe, Paul & Macchiarelli, Corrado, 2015. "Animal spirits and credit cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 59(C), pages 95-117.
    5. Isabelle SALLE & Marc-Alexandre SENEGAS & Murat YILDIZOGLU, 2013. "How Transparent About Its Inflation Target Should a Central Bank be? An Agent-Based Model Assessment," Cahiers du GREThA (2007-2019) 2013-24, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    6. repec:bdr:ensayo:v:35:y:2017:i:82:p:46-55 is not listed on IDEAS
    7. Tae-Seok Jang & Stephen Sacht, 2016. "Animal Spirits and the Business Cycle: Empirical Evidence from Moment Matching," Metroeconomica, Wiley Blackwell, vol. 67(1), pages 76-113, February.
    8. Anat Bracha & Jenny Tang, 2022. "Inflation Levels and (In)Attention," Working Papers 22-4, Federal Reserve Bank of Boston.
    9. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2014. "Inflation Expectations, Learning and Supermarket Prices," NBER Working Papers 20576, National Bureau of Economic Research, Inc.
    10. Deversi, Marvin, 2014. "Do Macroeconomic Shocks Affect Intuitive Inflation Forecasting? An Experimental Investigation," Ruhr Economic Papers 528, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    11. Salle, Isabelle & Yıldızoğlu, Murat & Sénégas, Marc-Alexandre, 2013. "Inflation targeting in a learning economy: An ABM perspective," Economic Modelling, Elsevier, vol. 34(C), pages 114-128.
    12. Isabelle Salle & Marc-Alexandre Sénégas & Murat Yıldızoğlu, 2019. "How transparent about its inflation target should a central bank be?," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 391-427, March.
    13. Rajat Deb & Prasenjit Deb & Sujit Majumder & Sourav Chakraborty & Kiran Sankar Chakraborty, 2019. "Answering Savings Puzzle About Small Saving Schemes and Mutual Funds: Evidence from Tripura," Metamorphosis: A Journal of Management Research, , vol. 18(1), pages 7-19, June.

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    More about this item

    Keywords

    Expectations; macroeconomic experiment; use of information; inflation forecasts;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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