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Separating Predicted Randomness from Noise

Author

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  • Jose Apesteguia
  • Miguel Ángel Ballester

Abstract

Given observed stochastic choice data and a model of stochastic choice, we offer a methodology that enables separation of the data representing the model's inherent randomness from residual noise, and thus quantify the maximal fraction of the data that are consistent with the model. We show how to apply our approach to any model of stochastic choice. We then study the case of four well-known models, each capturing a different notion of randomness. We conclude by illustrating our results with an experimental dataset.

Suggested Citation

  • Jose Apesteguia & Miguel Ángel Ballester, 2018. "Separating Predicted Randomness from Noise," Working Papers 1018, Barcelona School of Economics.
    • Handle: RePEc:bge:wpaper:1018
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    File URL: https://www.barcelonagse.eu/sites/default/files/working_paper_pdfs/1018.pdf
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    References listed on IDEAS

    as
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    5. Drew Fudenberg & Tomasz Strzalecki, 2015. "Dynamic Logit With Choice Aversion," Econometrica, Econometric Society, vol. 83, pages 651-691, March.
    6. Faruk Gul & Wolfgang Pesendorfer, 2006. "Random Expected Utility," Econometrica, Econometric Society, vol. 74(1), pages 121-146, January.
    7. Frederick Mosteller & Philip Nogee, 1951. "An Experimental Measurement of Utility," Journal of Political Economy, University of Chicago Press, vol. 59(5), pages 371-371.
    8. Paulo Natenzon, 2019. "Random Choice and Learning," Journal of Political Economy, University of Chicago Press, vol. 127(1), pages 419-457.
    9. Afriat, S N, 1973. "On a System of Inequalities in Demand Analysis: An Extension of the Classical Method," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(2), pages 460-472, June.
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    Cited by:

    1. Victor H. Aguiar & Maria Jose Boccardi & Nail Kashaev & Jeongbin Kim, 2023. "Random utility and limited consideration," Quantitative Economics, Econometric Society, vol. 14(1), pages 71-116, January.

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    More about this item

    Keywords

    stochastic choice; randomness; noise;
    All these keywords.

    JEL classification:

    • D00 - Microeconomics - - General - - - General

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