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This chapter provides an overview of the random utility maximisation (RUM) model, reviewing its assumptions and delving into its theoretical foundations. We explore the multinomial logit (MNL) model, which is widely used in DCE literature due to its many advantages. These include its robustness, ease of estimation, and straightforward interpretation, with closed-form choice probabilities that simplify calculations. We also review advanced specifications of the mixed logit model, including the random parameters logit (RP-MXL) and latent class (LC-MXL) models, and walk you through the most common goodness of fit models used in DCE research.

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Random Utility Models: Theoretical Background

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Petr Mariel,
  • Danny Campbell,
  • Erlend Dancke Sandorf,
  • Jürgen Meyerhoff,
  • Ainhoa Vega-Bayo,
  • Rebecca Blevins

摘要

This chapter provides an overview of the random utility maximisation (RUM) model, reviewing its assumptions and delving into its theoretical foundations. We explore the multinomial logit (MNL) model, which is widely used in DCE literature due to its many advantages. These include its robustness, ease of estimation, and straightforward interpretation, with closed-form choice probabilities that simplify calculations. We also review advanced specifications of the mixed logit model, including the random parameters logit (RP-MXL) and latent class (LC-MXL) models, and walk you through the most common goodness of fit models used in DCE research.