Improving out-of-sample predictions using response times and a model of the decision process

John A. Clithero

Journal of Economic Behavior & Organization, 2018, vol. 148, issue C, 344-375

Abstract: A basic problem in empirical economics involves using data from one domain to make out-of-sample predictions for a different, but related environment. When the choice data are binary, a canonical method for making these types of predictions is the logistic choice model. This paper investigates whether it is possible to improve out-of-sample predictions by changing two aspects of the canonical approach: 1) Using response times in addition to the choice data, and 2) Combining them using a model from the psychology and neuroscience literature, the Drift-Diffusion Model (DDM). Two experiments compare the out-of-sample choice prediction accuracies of both methods and in both cases the DDM method outperforms a logistic prediction method. Furthermore, the DDM allows for out-of-sample process predictions. Both experiments validate the DDM as a method for predicting out-of-sample response times.

Keywords: Drift diffusion; Neuroeconomics; Prediction; Response times (search for similar items in EconPapers)
JEL-codes: C9 D03 D87 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:148:y:2018:i:c:p:344-375

DOI: 10.1016/j.jebo.2018.02.007

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