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Separating Predicted Randomness from Residual Behavior

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  • Jose Apesteguia
  • Miguel A Ballester

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 in 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 data set.

Suggested Citation

  • Jose Apesteguia & Miguel A Ballester, 2021. "Separating Predicted Randomness from Residual Behavior," Journal of the European Economic Association, European Economic Association, vol. 19(2), pages 1041-1076.
    • Handle: RePEc:oup:jeurec:v:19:y:2021:i:2:p:1041-1076.
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    File URL: http://hdl.handle.net/10.1093/jeea/jvaa016
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    References listed on IDEAS

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    1. Jose Apesteguia & Miguel A. Ballester, 2018. "Monotone Stochastic Choice Models: The Case of Risk and Time Preferences," Journal of Political Economy, University of Chicago Press, vol. 126(1), pages 74-106.
    2. Filip Matêjka & Alisdair McKay, 2015. "Rational Inattention to Discrete Choices: A New Foundation for the Multinomial Logit Model," American Economic Review, American Economic Association, vol. 105(1), pages 272-298, January.
    3. Levon Barseghyan & Francesca Molinari & Ted O'Donoghue & Joshua C. Teitelbaum, 2018. "Estimating Risk Preferences in the Field," Journal of Economic Literature, American Economic Association, vol. 56(2), pages 501-564, June.
    4. Yoram Halevy & Dotan Persitz & Lanny Zrill, 2018. "Parametric Recoverability of Preferences," Journal of Political Economy, University of Chicago Press, vol. 126(4), pages 1558-1593.
    5. HORAN, Sean, 2018. "Threshold Luce rules," Cahiers de recherche 2018-17, Universite de Montreal, Departement de sciences economiques.
    6. Mark Dean & Daniel Martin, 2016. "Measuring Rationality with the Minimum Cost of Revealed Preference Violations," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 524-534, July.
    7. Frederick Mosteller & Philip Nogee, 1951. "An Experimental Measurement of Utility," Journal of Political Economy, University of Chicago Press, vol. 59, pages 371-371.
    8. Ian Crawford & Krishna Pendakur, 2013. "How many types are there?," Economic Journal, Royal Economic Society, vol. 123, pages 77-95, March.
    9. Famulari, Melissa, 1995. "A Household-Based, Nonparametric Test of Demand Theory," The Review of Economics and Statistics, MIT Press, vol. 77(2), pages 372-382, May.
    10. Paulo Natenzon, 2019. "Random Choice and Learning," Journal of Political Economy, University of Chicago Press, vol. 127(1), pages 419-457.
    11. Marina Agranov & Pietro Ortoleva, 2017. "Stochastic Choice and Preferences for Randomization," Journal of Political Economy, University of Chicago Press, vol. 125(1), pages 40-68.
    12. Ryan Webb, 2019. "The (Neural) Dynamics of Stochastic Choice," Management Science, INFORMS, vol. 65(1), pages 230-255, January.
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    Citations

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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.
    2. Jose Apesteguia & Miguel Ángel Ballester, 2020. "Random Utility Models with Ordered Types and Domains," Working Papers 1176, Barcelona School of Economics.
    3. Apesteguia, Jose & Ballester, Miguel A., 2023. "Random utility models with ordered types and domains," Journal of Economic Theory, Elsevier, vol. 211(C).
    4. Kashaev, Nail & Aguiar, Victor H., 2022. "A random attention and utility model," Journal of Economic Theory, Elsevier, vol. 204(C).
    5. Georgios Gerasimou, 2020. "The Decision-Conflict Logit," Papers 2008.04229, arXiv.org, revised Aug 2023.
    6. Leandro Nascimento, 2022. "Bounded arbitrage and nearly rational behavior," Papers 2212.02680, arXiv.org, revised Jul 2023.
    7. Efe A. Ok & Gerelt Tserenjigmid, 2023. "Measuring Stochastic Rationality," Papers 2303.08202, arXiv.org, revised Dec 2023.
    8. Petri, Henrik, 2023. "Binary single-crossing random utility models," Games and Economic Behavior, Elsevier, vol. 138(C), pages 311-320.
    9. Doğan, Serhat & Yıldız, Kemal, 2021. "Odds supermodularity and the Luce rule," Games and Economic Behavior, Elsevier, vol. 126(C), pages 443-452.

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    More about this item

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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