A comparison of approaches to estimating confidence intervals for willingness to pay measures
比较了四种估算支付意愿置信区间的方法（delta、Fieller、Krinsky Robb和bootstrap），通过模拟数据评估准确性，发现delta方法在数据良好时最准确，bootstrap对噪声数据和模型误设更稳健。
This paper describes four approaches to estimating confidence intervals for willingness to pay measures: the delta, Fieller, Krinsky Robb and bootstrap methods. The accuracy of the various methods is compared using a number of simulated datasets. In the majority of the scenarios considered all four methods are found to be reasonably accurate as well as yielding similar results. The delta method is the most accurate when the data is well-conditioned, while the bootstrap is more robust to noisy data and misspecification of the model. These conclusions are illustrated by empirical data from a study of willingness to pay for a reduction in waiting time for a general practitioner appointment in which all the methods produce fairly similar confidence intervals.