Estimation of Rank-Ordered Regret Minimization Models

• Published in: Computational economics Vol. 62; no. 4; pp. 1611 - 1630
• Main Authors: Liu, Changbiao, Li, Yuling
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2023; Springer Nature B.V
• Subjects: Behavioral/Experimental Economics; Bias; Computer Appl. in Social and Behavioral Sciences; Consumers; Data analysis; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Estimation; Logit models; Math Applications in Computer Science; Minimization; Monte Carlo simulation; Operations Research/Decision Theory; Optimization; Parameters; Ranking; Ratings & rankings; Regret; Root-mean-square errors; Simulation; Discrete choice; Rank-ordered; Random utility; Random regret minimization
• ISSN: 0927-7099; 1572-9974
• DOI: 10.1007/s10614-022-10313-y

This paper considers the estimation of random regret minimization models using rank-ordered choice data. By analyzing Monte Carlo simulations results, we find that the efficiency increases as we use additional information on the ranking. Compared with the multinomial logit model with utility maximization, the simulation results show that the standard random regret minimization model is slightly worse than the multinomial logit model based on both the mean bias and root mean squared error of the estimator of the model parameter β . When using long ranking choice data to estimate the random regret minimization model, based on the mean bias and root mean squared error of the estimator, we find that the rank-ordered random regret minimization model has advantages over the multinomial logit model and the standard random regret minimization model. Analysis of real data shows that our method is very effective in estimating model parameters.