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This chapter covers the estimation of discrete choice models. We begin with a simple Multinomial Logit (MNL) model (with and without unobserved preference heterogeneity) and highlight the importance of optimisation diagnostics, review goodness-of-fit indicators and the identification of outliers, and provide insights into the interpretation of estimates. Progressing to more advanced topics, we continue with the Random Parameters Mixed Logit (RP-MXL) model, addressing both uncorrelated and correlated coefficients, and discuss different parameterisation approaches such as the preference space and the willingness-to-pay space. We conclude with an analysis of Latent Class Mixed Logit (LC-MXL) models and a discussion of extensions of RP-MXL and LC-MXL models.

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Estimation

  • 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 covers the estimation of discrete choice models. We begin with a simple Multinomial Logit (MNL) model (with and without unobserved preference heterogeneity) and highlight the importance of optimisation diagnostics, review goodness-of-fit indicators and the identification of outliers, and provide insights into the interpretation of estimates. Progressing to more advanced topics, we continue with the Random Parameters Mixed Logit (RP-MXL) model, addressing both uncorrelated and correlated coefficients, and discuss different parameterisation approaches such as the preference space and the willingness-to-pay space. We conclude with an analysis of Latent Class Mixed Logit (LC-MXL) models and a discussion of extensions of RP-MXL and LC-MXL models.