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Fictive Learning in Choice under Uncertainty: A Logistic Regression Model

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Abstract

This paper is an exposition of an experiment on revealed preferences, where we posit a novel discrete binary choice model. To estimate this model, we use general estimating equations or GEE. This is a methodology originating in biostatistics for estimating regression models with correlated data. In this paper, we focus on the motivation for our approach, the logic and intuition underlying our analysis and a summary of our findings. The missing technical details are in the working paper by Bunn et al. (2013). The experimental data is available from the corresponding author: donald.brown@yale.edu. The recruiting poster and informed consent form are attached as appendices.

Suggested Citation

  • Donald J. Brown & Oliver Bunn & Caterina Calsamiglia & Donald J. Brown, 2013. "Fictive Learning in Choice under Uncertainty: A Logistic Regression Model," Cowles Foundation Discussion Papers 1890R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2014.
    • Handle: RePEc:cwl:cwldpp:1890r
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    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d18/d1890-r.pdf
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    References listed on IDEAS

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    1. Anat Bracha & Donald Brown, 2013. "Keynesian Utilities: Bulls and Bears," Levine's Working Paper Archive 786969000000000792, David K. Levine.
    2. Oliver Bunn & Caterina Calsamiglia & Donald J. Brown, 2013. "Testing for Fictive Learning in Decision-Making under Uncertainty," Cowles Foundation Discussion Papers 1890, Cowles Foundation for Research in Economics, Yale University.
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    1. Oliver Bunn & Caterina Calsamiglia & Donald J. Brown, 2013. "Testing for Fictive Learning in Decision-Making under Uncertainty," Cowles Foundation Discussion Papers 1890, Cowles Foundation for Research in Economics, Yale University.

    More about this item

    Keywords

    Counterfactual outcomes; Odds ratios; Alternating logistic regression;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles

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