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On the robustness of random regret minimization modelling outcomes towards omitted attributes

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  • van Cranenburgh, Sander
  • Prato, Carlo G.

Abstract

As discrete choice models may be misspecified, it is crucial for choice modellers to have knowledge on the robustness of their modelling outcomes towards misspecification. This study investigates the robustness of Random Regret Minimization (RRM) modelling outcomes towards one sort of model misspecification: the omission of relevant attributes. We explore the effect of omitted attributes (orthogonal and correlated) in the context of labelled and unlabeled data. In the context of labelled data, we show that – just as in RUM models – in RRM models Alternative Specific Constants (ASCs) can be used to capture the average effect of omitted attributes. However, in contrast to RUM models, ASCs in RRM models are choice set composition specific. This implies that in order to achieve consistent parameter estimates when the choice set composition varies across choice observations, different sets of ASCs need to be estimated for each unique choice set composition. In the context of unlabeled data, we show – using Monte Carlo simulations – that RRM models are fairly robust towards the presence of an orthogonal omitted attribute, though not as robust as the linear-additive RUM model. Specifically, we find that: (1) Aggregate Demand Elasticities (ADEs) implied by RRM models are less robust towards the presence of an orthogonal omitted attributes than those implied by linear-additive RUM models, and (2) Average Sample Effects (ASEs) implied by RRM models are – in the presence of an omitted orthogonal attribute – more sensitive towards misspecification in terms of the underlying decision rule than those implied by its linear-additive RUM counterpart.

Suggested Citation

  • van Cranenburgh, Sander & Prato, Carlo G., 2016. "On the robustness of random regret minimization modelling outcomes towards omitted attributes," Journal of choice modelling, Elsevier, vol. 18(C), pages 51-70.
    • Handle: RePEc:eee:eejocm:v:18:y:2016:i:c:p:51-70
    DOI: 10.1016/j.jocm.2016.04.004
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    References listed on IDEAS

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    1. Caspar G. Chorus & Sander Cranenburgh, 2018. "Specification of regret-based models of choice behaviour: formal analyses and experimental design based evidence—commentary," Transportation, Springer, vol. 45(1), pages 247-256, January.
    2. van Cranenburgh, Sander & Collins, Andrew T., 2019. "New software tools for creating stated choice experimental designs efficient for regret minimisation and utility maximisation decision rules," Journal of choice modelling, Elsevier, vol. 31(C), pages 104-123.
    3. Haghani, Milad & Sarvi, Majid, 2019. "Laboratory experimentation and simulation of discrete direction choices: Investigating hypothetical bias, decision-rule effect and external validity based on aggregate prediction measures," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 134-157.
    4. van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Does the decision rule matter for large-scale transport models?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 338-353.
    5. van Cranenburgh, Sander & Rose, John M. & Chorus, Caspar G., 2018. "On the robustness of efficient experimental designs towards the underlying decision rule," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 50-64.

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