Supervised Machine Learning for Eliciting Individual Reservation Values
64 Pages Posted: 13 May 2019
Date Written: April 29, 2019
Direct elicitation, guided by theory, is the standard method for eliciting individual-level latent variables. We present an alternative approach, supervised machine learning (SML), and apply it to measuring individual valuations for goods. We find that the approach is superior for predicting out-of-sample individual purchases relative to a canonical direct-elicitation approach, the Becker-DeGroot-Marschak (BDM) method. The BDM is imprecise and systematically biased by understating valuations. We characterize the performance of SML using a variety of estimation methods and data. The simulation results suggest that prices set by SML would increase revenue by 22% over the BDM, using the same data.
Keywords: Machine Learning, Willingness to Pay, BDM, Random Forest, Boosted Regression, Response Time
JEL Classification: C81, C91, D12
Suggested Citation: Suggested Citation