Separating Predicted Randomness from Noise
Jose Apesteguia, Universtat Pompeu Fabra
Micro Theory Seminar

with Miguel Ballester (Oxford) 

 

Abstract: Given stochastic behavior and a model of stochastic choice, we offer a methodology to separate from the data the randomness that is inherent to the stochastic choice model from what is noisy behavior. We then study the case of several choice models, and apply our methodology to an experimental dataset.

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University of Pennsylvania
3718 Locust Walk
Philadelphia, PA
Room: 410 McNeil Building
Date: Apr 25, 2017
Time:
Date: 
3:30pm - 5:00pm

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