Empirical underidentification in estimating random utility models: The role of choice sets and standardizations.

ABSTRACT: 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

SUBMITTER: Olschewski S 

PROVIDER: S-EPMC9298769 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature