Distribution-free Methods for Estimation of Willingness to Pay Models Using Discrete Response Valuation Data

The Turnbull method is the standard approach used in contingent valuation studies to estimate willingness to pay (WTP) models using discrete responses without making assumptions about the distribution of the data. However, this approach has several limitations. The purpose of this study is to develop alternative distribution-free methods for the estimation of WTP models using nonparametric conditional imputation and local regression procedures. The proposed approaches encompass the recovery of the individuals’ WTP values using an iterated conditional expectation procedure and subsequent estimation of the mean WTP using linear and nonparametric additive models. In contrast to the Turnbull approach, the proposed estimation methods allow the inclusion of covariates in the modeling of WTP estimates, as well as the complete recovery of its underlying probability distribution. Monte Carlo simulations are employed to compare the performance of the proposed estimators with that of the Turnbull estimator. We also illustrate the use of the proposed estimation techniques using a real data set.