CFDP 1890R

Fictive Learning in Choice under Uncertainty: A Logistic Regression Model

Author(s): 

Publication Date: March 2013

Revision Date: March 2014

Pages: 14

Abstract: 

This paper is an exposition of an experiment on revealed preferences, where we posite 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

JEL Classification Codes:  C23, C35, C91, D03