There is a growing literature that uses stated preference surveys to estimate discount rates. A review of the literature reveals large variation both in the discount rate estimates coming from different stated preference surveys and in the specific empirical methodologies used to estimate discount rates. While most use similar theory and logic in deriving discount rate estimates, it remains an open question how much of the variation seen in the literature is due to differences in empirical methodology. Using a single data set, we estimate annual discount rates using six different methodologies, including endogenous and ex-post estimation techniques as well as a variety of parametric and nonparametric choice models. We find that most of our estimates are tightly clustered between 14.5 and 31% while one methodology yields an outlier value of 200%. We also use multiple metrics to determine which methodology yields the “right” discount rate and find that the methodology with the best “goodness-of-fit” using information statistics does not always yield the highest predictive accuracy. Our findings suggest that, while methods grounded in similar theory often produce comparable methods, caution and robustness checks are critical.

越来越多的文献使用陈述的偏好调查来估计折现率。文献回顾显示，来自不同陈述偏好调查的折现率估算值以及用于估算折现​​率的特定经验方法均存在很大差异。尽管大多数人在得出折现率估​​计时使用相似的理论和逻辑，但仍存在一个悬而未决的问题，即文献中看到的多少差异是由于经验方法的差异所致。使用单个数据集，我们使用六种不同的方法来估算年贴现率，包括内生和事后估计技术以及各种参数和非参数选择模型。我们发现我们的大多数估计值都紧密地集中在14.5％和31％之间，而一种方法得出的离群值是200％。我们还使用多个指标来确定哪种方法产生“正确的”贴现率，并发现使用信息统计数据具有最佳“拟合优度”的方法并不总是产生最高的预测准确性。我们的发现表明，尽管基于相似理论的方法通常可以产生可比的方法，但是谨慎性和鲁棒性检查至关重要。