Structural methods to estimate preferences for decisions under risk often perform poorly when predicting new decisions out of sample. We compare standard structural methods to a new nonparametric method that reveals preferences with the help of response times, and does so independently of any utility or (symmetric) noise specification. Using two datasets involving choices over risky prospects, we show that the new method outperforms standard (parametric) utility estimations in terms of predictive accuracy.
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