A General Framework for Estimating Preferences Using Response Time Data

TL;DR

This paper introduces a versatile framework for estimating preferences from choice and response time data, demonstrating fast convergence and broad applicability, with an empirical case showing improved predictive accuracy and parameter estimation.

Contribution

It presents a general methodology for preference estimation using response times, applicable to various decision models including the DDM, with proven convergence rates and empirical validation.

Findings

Response time data improves predictive accuracy.

Estimates achieve $1/n$ convergence rate.

Empirical application on intertemporal choice confirms usefulness.

Abstract

We propose a general methodology for recovering preference parameters from data on choices and response times. Our methods yield estimates with fast ($1/n$ for $n$ data points) convergence rates when specialized to the popular Drift Diffusion Model (DDM), but are broadly applicable to generalizations of the DDM as well as to alternative models of decision making that make use of response time data. The paper develops an empirical application to an experiment on intertemporal choice, showing that the use of response times delivers predictive accuracy and matters for the estimation of economically relevant parameters.