Data and code for: A Unified Model of Learning to Forecast

Principal Investigator(s):  View help for Principal Investigator(s) George W. Evans, University of Oregon; Christopher Giorgio Gibbs, University of Sydney; Bruce McGough, University of Oregon

Version:  View help for Version V1

Name File Type Size Last Modified
  Clean Data 02/08/2024 06:24:PM
  Programs 04/17/2024 09:33:PM
  Raw Data 02/08/2024 06:26:PM
  Simulations 02/08/2024 06:26:PM
  oTree Source Code 02/08/2024 06:23:PM
Appendix_Figures_and_Tables.do text/plain 2.6 KB 02/08/2024 01:23:PM
Data_clearning.do text/plain 1.2 KB 02/08/2024 01:23:PM
Figure_2.do text/plain 855 bytes 02/08/2024 01:23:PM
Figure_2_Tables_3_A5_A6.nb application/mathematica 182.5 KB 02/08/2024 01:23:PM
README.pdf application/pdf 114.7 KB 04/17/2024 10:16:PM

Project Citation: 

Evans, George W. , Gibbs, Christopher Giorgio, and McGough, Bruce . Data and code for: A Unified Model of Learning to Forecast. Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-03-12. https://doi.org/10.3886/E198204V1

Project Description

Summary:  View help for Summary We propose 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. 

This repository contains the raw data of the experiment to the test the unified model. It contains experimental data collected in the UNSW Sydney Business School BizLab in 2018 and in the University of Sydney School of Economics Experimental Lab in 2019. Subject played a learning-to-forecast game, where they were paid based on the accuracy of their forecasts. The subjects forecasted the price of a good traded in simple demand and supply dynamic market environment with a production lag. The market featured structural changes to the environment, which were fully known to subjects. How subjects' forecasts incorporated the information about the structural change is the main question of interest.
Funding Sources:  View help for Funding Sources Australian Research Council (DP210101204); National Science Foundation (SES-1559209)

Scope of Project

Subject Terms:  View help for Subject Terms adaptive learning; level-k reasoning; behavioral macroeconomics; forward guidance; experiment
JEL Classification:  View help for JEL Classification
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
      D84 Expectations; Speculations
      E31 Price Level; Inflation; Deflation
      E32 Business Fluctuations; Cycles
      E52 Monetary Policy
      E71 Macro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on the Macro Economy
Geographic Coverage:  View help for Geographic Coverage Australia
Collection Date(s):  View help for Collection Date(s) 5/23/2018 – 3/22/2019
Data Type(s):  View help for Data Type(s) experimental data

Methodology

Sampling:  View help for Sampling Laboratory experiment
Collection Mode(s):  View help for Collection Mode(s) coded on-site observation

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Published Versions

V1 [2025-03-12]

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