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Adaptive Learning and Survey Data

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  • Agnieszka Markiewicz

    (Erasmus University Rotterdam)

  • Andreas Pick

    (Erasmus University Rotterdam and De Nederlandsche Bank)

Abstract

This paper investigates the ability of the adaptive learning approach to replicate the expectations of professional forecasters. For a range of macroeconomic and financial variables, we compare constant and decreasing gain learning models to simple, yet powerful benchmark models. We find that both, constant and decreasing gain models, provide a good fit for the expectations of professional forecasters for a range of variables. These results suggest that, instead of relying only on the the most recent observation, agents use more complex models to form their expectations even for financial variables where random walk forecasts are often difficult to beat.

Suggested Citation

  • Agnieszka Markiewicz & Andreas Pick, 2013. "Adaptive Learning and Survey Data," CDMA Working Paper Series 201305, Centre for Dynamic Macroeconomic Analysis.
  • Handle: RePEc:san:cdmawp:1305
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    1. Adaptive Learning and Survey Data
      by Alessandro Cerboni in Knowledge Team on 2013-09-16 23:03:19

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    Cited by:

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    3. Kuang, Pei & Mitra, Kaushik, 2016. "Long-run growth uncertainty," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 67-80.
    4. Norbert Christopeit & Michael Massmann, 2013. "Estimating Structural Parameters in Regression Models with Adaptive Learning," Tinbergen Institute Discussion Papers 13-111/III, Tinbergen Institute.
    5. André, Marine Charlotte & Dai, Meixing, 2017. "Is central bank conservatism desirable under learning?," Economic Modelling, Elsevier, vol. 60(C), pages 281-296.
    6. Koursaros, Demetris, 2019. "Learning expectations using multi-period forecasts," Journal of Economics and Business, Elsevier, vol. 102(C), pages 1-25.
    7. Ali, Syed Zahid & Anwar, Sajid, 2017. "Exchange rate pass through, cost channel to monetary policy transmission, adaptive learning, and the price puzzle," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 69-82.
    8. Michele Berardi, 2020. "A probabilistic interpretation of the constant gain learning algorithm," Bulletin of Economic Research, Wiley Blackwell, vol. 72(4), pages 393-403, October.
    9. Gelfer, Sacha, 2020. "The effects of professional forecast dissemination on macroeconomic volatility," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 131-156.
    10. Stephen J. Cole & Fabio Milani, 2020. "Heterogeneity in Individual Expectations, Sentiment, and Constant-Gain Learning," Working Papers 192005, University of California-Irvine, Department of Economics.
    11. Berardi, Michele & Galimberti, Jaqueson K., 2014. "A note on the representative adaptive learning algorithm," Economics Letters, Elsevier, vol. 124(1), pages 104-107.
    12. Alex Ilek, 2020. "Are monetary surprises effective? The view of professional forecasters in Israel," Bank of Israel Working Papers 2020.09, Bank of Israel.
    13. Marine Charlotte André & Meixing Dai, 2015. "Central bank accountability under adaptive learning," Working Papers of BETA 2015-32, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    14. Marine Charlotte André & Meixing Dai, 2017. "Learning, optimal monetary delegation and stock prices dynamics," Working Papers of BETA 2017-37, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

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    More about this item

    Keywords

    expectations; survey of professional forecasters; adaptive learning; bounded rationality;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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