A Monte Carlo analysis is conducted to assess the validity of the bivariate modeling approach for detection and correction of different forms of elicitation effects in Double Bound Contingent Valuation data. Alternative univariate and bivariate models are applied to several simulated data sets, each one characterized by a specific elicitation effect, and their performance is assessed using standard selection criteria. The bivariate models include the standard Bivariate Probit model, and an alternative specification, based on the Copula approach to multivariate modeling, which is shown to be useful in cases where the hypothesis of normality of the joint distribution is not supported by the data. It is found that the bivariate approach can effectively correct elicitation effects while maintaining an adequate level of efficiency in the estimation of the parameters of interest.