Fictive Learning in Choice under Uncertainty: A Logistic Regression Model

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