Fictive Learning in Choice under Uncertainty: A Logistic Regression Model

15 Pages Posted: 22 Mar 2014

See all articles by Donald Brown

Donald Brown

Yale University - Cowles Foundation

Oliver Bunn

Yale University - Department of Economics

Caterina Calsamiglia

Universitat Autònoma de Barcelona

Date Written: March 21, 2014

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. The recruiting poster and informed consent form are attached as appendices.

Keywords: Counterfactual outcomes, Odds ratios, Alternating logistic regression

JEL Classification: C23, C35, C91, D03

Suggested Citation

Brown, Donald J. and Bunn, Oliver and Calsamiglia, Caterina, Fictive Learning in Choice under Uncertainty: A Logistic Regression Model (March 21, 2014). Cowles Foundation Discussion Paper No. 1890R, Available at SSRN: https://ssrn.com/abstract=2412506 or http://dx.doi.org/10.2139/ssrn.2412506

Donald J. Brown (Contact Author)

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States

Oliver Bunn

Yale University - Department of Economics ( email )

28 Hillhouse Ave
New Haven, CT 06520-8268
United States

Caterina Calsamiglia

Universitat Autònoma de Barcelona ( email )

Edifici B - Campus Bellaterra
Barcelona, 08193
Spain

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