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

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  • Clithero, John A.

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.

Suggested Citation

  • Clithero, John A., 2018. "Improving out-of-sample predictions using response times and a model of the decision process," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 344-375.
  • Handle: RePEc:eee:jeborg:v:148:y:2018:i:c:p:344-375
    DOI: 10.1016/j.jebo.2018.02.007
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    References listed on IDEAS

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    More about this item

    Keywords

    Drift diffusion; Neuroeconomics; Prediction; Response times;

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics

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