Paper #1757
- Title:
- Separating predicted randomness from residual behavior
- Authors:
- Jose Apesteguia and Miguel Ángel Ballester
- Date:
- February 2020
- Abstract:
- We propose a novel measure of goodness of fit for stochastic choice models: that is, the maximal fraction of data that can be reconciled with the model. The procedure is to separate the data into two parts: one generated by the best specification of the model and another representing residual behavior. We claim that the three elements involved in a separation are instrumental to understanding the data. We show how to apply our approach to any stochastic choice model and then study the case of four well-known models, each capturing a different notion of randomness. We illustrate our results with an experimental dataset.
- Keywords:
- Goodness of fit; Stochastic Choice; Residual Behavior
- JEL codes:
- C91, D81, G12, G20, G41
- Area of Research:
- Microeconomics
- Published in:
- Journal of the European Economic Association, forthcoming.
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