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Empirical Application of Random Regret Minimization-Models

In: Random Regret-based Discrete Choice Modeling

Author

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  • Caspar G. Chorus

    (Delft University of Technology)

Abstract

This chapter presents an in-depth discussion of how the RRM-based MNL-model is estimated, and how estimation results are interpreted and used for forecasting. This is done by means of a comprehensive discussion of one running example. Section 3.1 presents the dataset used for empirical analyses, while Sect. 3.2 discusses RRM-model estimation. Sections 3.3 and 3.4 discuss model fit, respectively the interpretation of estimation results, and Sect. 3.5 shows how estimated models can be used to forecast market shares (highlighting some of the RRM-model’s important empirical properties). Section 3.6 concludes by discussing the out-of-sample validity of the RRM-model on the given dataset. Comparisons with the RUM-based MNL-model are provided throughout.

Suggested Citation

  • Caspar G. Chorus, 2012. "Empirical Application of Random Regret Minimization-Models," SpringerBriefs in Business, in: Random Regret-based Discrete Choice Modeling, edition 127, chapter 0, pages 17-34, Springer.
    • Handle: RePEc:spr:spbrcp:978-3-642-29151-7_3
    DOI: 10.1007/978-3-642-29151-7_3
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