Single or double bounded contingent valuation? A Bayesian test

This paper evaluates the performance of asymptotic approximations of the Bayes factor to appraise the relative likelihoods of the bivariate and the restricted double bounded models for contingent valuation. The performance of the Bayes factor test is studied by Monte Carlo simulation showing that it correctly chooses the bivariate model when appropriate, but tends to over predict the double bounded model when the correlation coefficient is not estimated accurately. However, the quadratic error in estimating willingness to pay is reduced if the model preferred by the test is chosen. In addition, we consider the effect of averaging the estimates of WTP from both models, weighting each model with its posterior probability. The results show that ‘model averaging’ across the competing hypothesis further reduces the squared error. The applications with two data sets on National Parks show that the test rejects the restricted double bounded hypotheses against the bivariate model.