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A Unified Model of Learning to Forecast

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  • Evans, George
  • Gibbs, Christopher
  • McGough, Bruce

Abstract

We propose and experimentally test a model of boundedly rational and heterogeneous expectations that unifies adaptive learning, k-level reasoning, and replicator dynamics. Level-0 forecasts evolve over time via adaptive learning. Agents revise over time their depth of reasoning in response to forecast errors, observed and counterfactual. The unified model makes sharp predictions for when and how fast markets converge in Learning-to-Forecast Experiments, including novel predictions for individual and market behavior in response to announced events. The experimental results support these predictions. Our unified model is developed in a simple framework, but can clearly be extended to more general macroeconomic environments.

Suggested Citation

  • Evans, George & Gibbs, Christopher & McGough, Bruce, 2021. "A Unified Model of Learning to Forecast," Working Papers 2021-10, University of Sydney, School of Economics.
    • Handle: RePEc:syd:wpaper:2021-10
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    2. Eusepi, Stefano & Gibbs, Chris & Preston, Bruce, 2021. "Forward guidance with unanchored expectations," Research Discussion Papers 11/2021, Bank of Finland.

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    Keywords

    expectations; adaptive learning; level-k reasoning; behavioral macroeconomics; experiments;
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