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Assessing the forecast performance of models of choice

Dale O. Stahl

Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), 2018, vol. 73, issue C, 86-92

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.

Keywords: Forecast performance; Over-fitting; Cross-validation; Lottery choice (search for similar items in EconPapers)
JEL-codes: C52 C53 C91 D81 (search for similar items in EconPapers)
Date: 2018
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