Empirical underidentification in estimating random utility models: The role of choice sets and standardizations. (8th November 2021)

• Record Type: Journal Article
• Authors: Olschewski, Sebastian; Sirotkin, Pavel; Rieskamp, Jörg
• Is Part Of: British journal of mathematical & statistical psychology. Volume 75:Part 2(2022)
• Page Start: 252
• Page End: 292
• Publication Date: 2021-11-08
• Subjects: correlation of estimates -- estimation technique -- power utility -- random utility model -- risky choice; Psychometrics -- Periodicals; Psychology -- Mathematical models -- Periodicals; Psychology -- Statistical methods -- Periodicals; 150.727
• DOI: 10.1111/bmsp.12256
• Languages: English
• ISSNs: 0007-1102

Abstract

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 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.