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

60 Pages Posted: 22 Jun 2016 Last revised: 9 Feb 2018

See all articles by John A. Clithero

John A. Clithero

Lundquist College of Business, University of Oregon

Date Written: February 5, 2018

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

Suggested Citation

Clithero, John A., Improving Out-of-Sample Predictions Using Response Times and a Model of the Decision Process (February 5, 2018). Available at SSRN: https://ssrn.com/abstract=2798459 or http://dx.doi.org/10.2139/ssrn.2798459

John A. Clithero (Contact Author)

Lundquist College of Business, University of Oregon ( email )

Lundquist College of Business
1208 University of Oregon
Eugene, OR 97403
United States

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