Fictive Learning in Choice under Uncertainty: A Logistic Regression Model
CFDP Revision Date
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: firstname.lastname@example.org . The recruiting poster and informed consent form are attached as appendices.
Brown, Donald J.; Bunn, Oliver D.; and Calsamiglia, Caterina, "Fictive Learning in Choice under Uncertainty: A Logistic Regression Model" (2013). Cowles Foundation Discussion Papers. 2263.