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randregret: A command for fitting random regret minimization models using Stata

Author

Listed:
  • Álvaro A. Gutiérrez-Vargas

    (KU Leuven)

  • Michel Meulders

    (KU Leuven)

  • Martina Vandebroek

    (KU Leuven)

Abstract

In this article, we describe the randregret command, which imple- ments a variety of random regret minimization (RRM) models. The command allows the user to apply the classic RRM model introduced in Chorus (2010, Eu- ropean Journal of Transport and Infrastructure Research 10: 181–196), the gen- eralized RRM model introduced in Chorus (2014, Transportation Research, Part B 68: 224–238), and also the μRRM and pure RRM models, both introduced in van Cranenburgh, Guevara, and Chorus (2015, Transportation Research, Part A 74: 91–109). We illustrate the use of the randregret command by using stated choice data on route preferences. The command offers robust and cluster standard- error correction using analytical expressions of the score functions. It also offers likelihood-ratio tests that can be used to assess the relevance of a given model spec- ification. Finally, users can obtain the predicted probabilities from each model by using the randregretpred command.

Suggested Citation

  • Álvaro A. Gutiérrez-Vargas & Michel Meulders & Martina Vandebroek, 2021. "randregret: A command for fitting random regret minimization models using Stata," Stata Journal, StataCorp LP, vol. 21(3), pages 626-658, September.
    • Handle: RePEc:tsj:stataj:v:21:y:2021:i:3:p:626-658
    DOI: 10.1177/1536867X211045538
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    Cited by:

    1. Álvaro A. Gutiérrez-Vargas & Ziyue Zhu & Martina Vandebroek, 2022. "mixrandregret: A command for fitting mixed random regret minimization models using Stata," London Stata Conference 2022 17, Stata Users Group.
    2. Ziyue Zhu & 'Alvaro A. Guti'errez-Vargas & Martina Vandebroek, 2023. "Fitting mixed logit random regret minimization models using maximum simulated likelihood," Papers 2301.01091, arXiv.org.

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