A commonly used sampling design in economic valuation studies such as theCVM, is on-site sampling. If this sampling design is used, the sampling inclu-sion probabilities may be correlated with respondents’ valuations, invalidatingwelfare measures derived from estimates of the probit model. This paper pro-poses a model based on the bivariate ordinal probit, a model that can be usedto analyze binary choice CV data gathered by on-site sampling. This paperpresents the model, derives the log-likelihood, and illustrates the MLE using asmall simulation study. The model is presented, the log-likelihood is derived andthe properties of the MLE’s are illustrated with a small simulation study. Thesimulation results show the proposed estimator to be an interesting alternative,but the estimator should be evaluated in more population models.
A commonly used sampling design in economic valuation studies is on-sitesampling. If this sampling design is used, the sampling inclusion probabil-ities may be correlated with respondents’ valuations, invalidating welfaremeasures derived from estimates of the probit model. This problem is re-ferred to a length-bias, a problem discovered in other fields of applicationof statistics.The first paper in this thesis outlines different application fields thathave length-bias problems and the suggested model solutions in the litera-ture are presented.The second paper of this thesis proposes a model based on the bivariateordinal probit, a model that can be used to analyze binary choice CV datagathered by on-site sampling. The models is presented, the log-likelihoodis derived, and the properties of the MLE’s are illustrated using a smallsimulation study. The simulation results show the proposed estimator tobe an interesting alternative.