Center for Economic Psychology, University of Basel, Switzerland.
Warwick Business School, University of Warwick, Coventry, UK.
Br J Math Stat Psychol. 2022 May;75(2):252-292. doi: 10.1111/bmsp.12256. Epub 2021 Nov 8.
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.
区分人们风险偏好的一种标准方法是使用幂效用函数来描述偏好，并使用逻辑函数来捕捉选择一致性，从而估计随机效用模型。我们证明，对于常用的选择情况，该模型存在经验上的无法识别问题，这意味着参数无法精确估计。我们通过对估计准确性和 Kullback-Leibler 散度的模拟，研究了潜在缓解该问题的因素。首先，使用选择集来保证在合理范围内的两个风险赌博之间的效用顺序发生变化，比随机创建的选择集或文献中常见的专用选择集更能提高估计准确性。其次，参数估计值通常相关，这导致了经验上的无法识别。我们通过对效用尺度的标准化进行研究，表明它们可以缓解这种相关性，并进一步提高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，我们还展示了重复与不同的选择集以及观测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，以更精确地估计随机效用模型中的参数。
