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randregret: A command for fitting Random Regret Minimization Models

Author

Listed:
  • Álvaro A. Gutiérrez Vargas

    (Centre for Research Operation and Statistics (ORSTAT), KU Leuven, Belgium)

  • Michel Meulders

    (Centre for Research Operation and Statistics (ORSTAT), KU Leuven, Belgium)

  • Martina Vandebroek

    (Centre for Research Operation and Statistics (ORSTAT), KU Leuven, Belgium)

Abstract

In this article, we describe the randregret command which implements a variety of Random Regret Minimization (RRM) models. The command allows the user to apply the classic RRM model (Chorus, 2010), the Generalized RRM model (Chorus, 2014), and also the mu-RRM and Pure RRM models (Van Cranenburgh, Guevara and Chorus, 2015). We illustrate the usage of the randregret command 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 which can be used to assess the relevance of a given model specification. Finally, predicted probabilities from each model can be easily computed using the randregretpred postestimation command.

Suggested Citation

  • Álvaro A. Gutiérrez Vargas & Michel Meulders & Martina Vandebroek, 2020. "randregret: A command for fitting Random Regret Minimization Models," London Stata Conference 2020 13, Stata Users Group.
    • Handle: RePEc:boc:usug20:13
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    References listed on IDEAS

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    1. Chorus, Caspar G., 2014. "A Generalized Random Regret Minimization model," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 224-238.
    2. Roberto G. Gutierrez & Shana Carter & David M. Drukker, 2001. "On boundary-value likelihood-ratio tests," Stata Technical Bulletin, StataCorp LP, vol. 10(60).
    3. van Cranenburgh, Sander & Guevara, Cristian Angelo & Chorus, Caspar G., 2015. "New insights on random regret minimization models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 91-109.
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