A standard approach to distinguishing people’s risk preferences is to estimate a random utility model using a power utility function to characterize the preferences and a logit function to capture choice consistency. We demonstrate that with often-used choice situations, this model suffers from empirical underidentification, meaning that parameters cannot be estimated precisely. With simulations of estimation accuracy and Kullback–Leibler divergence measures we examined factors that potentially mitigate this problem. First, using a choice set that guarantees a switch in the utility order between two risky gambles in the range of plausible values leads to higher estimation accuracy than randomly created choice sets or the purpose-built choice sets common in the literature. Second, parameter estimates are regularly correlated, which contributes to empirical underidentification. Examining standardizations of the utility scale, we show that they mitigate this correlation and additionally improve the estimation accuracy for choice consistency. Yet, they can have detrimental effects on the estimation accuracy of risk preference. Finally, we also show how repeated versus distinct choice sets and an increase in observations affect estimation accuracy. Together, these results should help researchers make informed design choices to estimate parameters in the random utility model more precisely.

中文翻译：

---

估计随机效用模型中的经验不足：选择集和标准化的作用


区分人们风险偏好的标准方法是使用幂效用函数来表征偏好并使用 logit 函数来捕获选择一致性来估计随机效用模型。我们证明，在经常使用的选择情况下，该模型存在经验识别不足的问题，这意味着无法精确估计参数。通过对估计精度和 Kullback-Leibler 散度测量的模拟，我们研究了可能缓解此问题的因素。首先，使用保证在合理值范围内的两个风险赌博之间效用顺序切换的选择集，比随机创建的选择集或文献中常见的专门构建的选择集具有更高的估计精度。其次，参数估计通常是相关的，这导致了经验识别不足。通过检查效用规模的标准化，我们发现它们减轻了这种相关性，并另外提高了选择一致性的估计准确性。然而，它们可能会对风险偏好的估计准确性产生不利影响。最后，我们还展示了重复选择集与不同选择集以及观察次数的增加如何影响估计准确性。总之，这些结果应该有助于研究人员做出明智的设计选择，以更准确地估计随机效用模型中的参数。