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Estimation of Rank-Ordered Regret Minimization Models

Author

Listed:
  • Changbiao Liu

    (Guangxi University of Finance and Economics)

  • Yuling Li

    (Beijing Normal University)

Abstract

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 $${{\varvec{\beta }}}$$ 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.

Suggested Citation

  • Changbiao Liu & Yuling Li, 2023. "Estimation of Rank-Ordered Regret Minimization Models," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1611-1630, December.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:4:d:10.1007_s10614-022-10313-y
    DOI: 10.1007/s10614-022-10313-y
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    References listed on IDEAS

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    1. Matthew J. Beck & Caspar G. Chorus & John M. Rose & David A. Hensher, 2013. "Vehicle Purchasing Behaviour of Individuals and Groups: Regret or Reward?," Journal of Transport Economics and Policy, University of Bath, vol. 47(3), pages 475-492, September.
    2. Boeri, Marco & Longo, Alberto, 2017. "The importance of regret minimization in the choice for renewable energy programmes: Evidence from a discrete choice experiment," Energy Economics, Elsevier, vol. 63(C), pages 253-260.
    3. Loomes, Graham & Sugden, Robert, 1982. "Regret Theory: An Alternative Theory of Rational Choice under Uncertainty," Economic Journal, Royal Economic Society, vol. 92(368), pages 805-824, December.
    4. Caspar Chorus & Michel Bierlaire, 2013. "An empirical comparison of travel choice models that capture preferences for compromise alternatives," Transportation, Springer, vol. 40(3), pages 549-562, May.
    5. Dries F. Benoit & Stefan Van Aelst & Dirk Van den Poel, 2016. "Outlier‐Robust Bayesian Multinomial Choice Modeling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1445-1466, November.
    6. Lane F. Burgette & Erik V. Nordheim, 2012. "The Trace Restriction: An Alternative Identification Strategy for the Bayesian Multinomial Probit Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 404-410, February.
    7. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    8. Yan, Jin & Yoo, Hong Il, 2019. "Semiparametric estimation of the random utility model with rank-ordered choice data," Journal of Econometrics, Elsevier, vol. 211(2), pages 414-438.
    9. Greg M. Allenby & Peter E. Rossi, 1991. "Quality Perceptions and Asymmetric Switching Between Brands," Marketing Science, INFORMS, vol. 10(3), pages 185-204.
    10. Dennis Fok & Richard Paap & Bram Van Dijk, 2012. "A Rank‐Ordered Logit Model With Unobserved Heterogeneity In Ranking Capabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 831-846, August.
    11. Chorus, Caspar G. & Arentze, Theo A. & Timmermans, Harry J.P., 2008. "A Random Regret-Minimization model of travel choice," Transportation Research Part B: Methodological, Elsevier, vol. 42(1), pages 1-18, January.
    12. Arie Beresteanu & Federico Zincenko, 2018. "Efficiency Gains in Rank†ordered Multinomial Logit Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(1), pages 122-134, February.
    13. Hauser, John R & Wernerfelt, Birger, 1990. "An Evaluation Cost Model of Consideration Sets," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(4), pages 393-408, March.
    14. Skelton, Alexandra C.H. & Allwood, Julian M., 2017. "Questioning demand: A study of regretted purchases in Great Britain," Ecological Economics, Elsevier, vol. 131(C), pages 499-509.
    15. Beggs, S. & Cardell, S. & Hausman, J., 1981. "Assessing the potential demand for electric cars," Journal of Econometrics, Elsevier, vol. 17(1), pages 1-19, September.
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