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Efficiency Gains in Rank†ordered Multinomial Logit Models

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  • Arie Beresteanu
  • Federico Zincenko

Abstract

This paper considers estimation of discrete choice models when agents report their ranking of the alternatives (or some of them) rather than just the utility maximizing alternative. We investigate the parametric conditional rank†ordered Logit model. We show that conditions for identification do not change even if we observe ranking. Moreover, we fill a gap in the literature and show analytically and by Monte Carlo simulations that efficiency increases as we use additional information on the ranking.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:obuest:v:80:y:2018:i:1:p:122-134
    DOI: 10.1111/obes.12190
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    Cited by:

    1. 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.
    2. Hunt, Priscillia E & Smart, Rosanna, 2020. "Investigation of Employers' Preferences for the Design of Staffing Agency Incentives to Hire Ex-Felons," IZA Discussion Papers 13520, Institute of Labor Economics (IZA).
    3. Xing, Jianwei & Leard, Benjamin & Li, Shanjun, 2021. "What does an electric vehicle replace?," Journal of Environmental Economics and Management, Elsevier, vol. 107(C).
    4. 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.

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