Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7241989 | Journal of Behavioral and Experimental Economics | 2018 | 7 Pages |
Abstract
We often want to predict human behavior. It is well-known that the model that fits in-sample data best is not necessarily the model that forecasts (i.e. predicts out-of-sample) best, but we lack guidance on how to select a model for the purpose of forecasting. We illustrate the general issues and methods with the case of Rank-Dependent Expected Utility versus Expected Utility, using laboratory data and simulations. We find that poor forecasting performance is a likely outcome for typical laboratory sample sizes due to over-fitting. Finally we derive a decision-theory-based rule for selecting the best model for forecasting depending on the sample size.
Related Topics
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Authors
Dale O. Stahl,