Paper #1757

Título:

Separating predicted randomness from residual behavior

Autores:

Jose Apesteguia y Miguel Ángel Ballester

Fecha:

Febrero 2020

Resumen:

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.

Palabras clave:

Goodness of fit; Stochastic Choice; Residual Behavior

Códigos JEL:

C91, D81, G12, G20, G41

Área de investigación:

Microeconomía

Publicado en:

Journal of the European Economic Association, forthcoming.

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