This paper systematically compares methods to build confidence intervals for willingness to
pay measures in a discrete choice context. It contributes to the literature by including methods
developed in other research fields. Monte Carlo simulations are used to assess the performance
of all the methods considered. The various scenarios evaluated reveal a certain skewness in the
estimated willingness to pay distribution. This should be reflected in the confidence intervals.
Results show that the commonly used Delta method, producing symmetric intervals around
the point estimate, often fails to account for such a skewness. Both the Fieller method and
the likelihood ratio test inversion method produce more realistic confidence intervals. Some
bootstrap methods also perform reasonably well. Finally, empirical data are used to illustrate
an application of the methods considere
