Improving Out-of-Sample Predictions Using Response Times and a Model of the Decision Process

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, stochastic choice

JEL Classification: C9, D03, D87