A General Framework for Estimating Preferences Using Response Time Data
This work addresses the challenge of accurately estimating preferences in economic and decision-making contexts, offering a broadly applicable framework that enhances parameter estimation through response time data, though it appears incremental as it builds on existing models like the DDM.
The authors tackled the problem of estimating preference parameters from choice and response time data, proposing a general methodology that achieves fast convergence rates (1/n) when applied to the Drift Diffusion Model and other decision-making models, with an empirical application showing improved predictive accuracy and impact on economically relevant parameters.
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.