Fictive Learning in Choice under Uncertainty: A Logistic Regression Model

Donald J. Brown (donald.brown@yale.edu), Oliver Bunn, Caterina Calsamiglia and Donald J. Brown
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Donald J. Brown: Dept. of Economics, Yale University, https://economics.yale.edu/people/emeritus/donald-j-brown
Oliver Bunn: Dept. of Economics, Yale University

No 1890R, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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.

Keywords: Counterfactual outcomes; Odds ratios; Alternating logistic regression (search for similar items in EconPapers)
JEL-codes: C23 C35 C91 D03 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2013-03, Revised 2014-03
New Economics Papers: this item is included in nep-dcm, nep-exp and nep-ore
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