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 |
|---|---|---|---|
| oTree | 02/08/2024 06:32:PM |
Project Description
Summary:
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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.
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.
Scope of Project
Subject Terms:
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adaptive learning;
level-k reasoning;
behavioral macroeconomics;
forward guidance;
experiment
JEL Classification:
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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
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:
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Australia
Data Type(s):
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experimental data
Methodology
Sampling:
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Laboratory experiment
Collection Mode(s):
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coded on-site observation
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