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This chapter highlights the importance of the post-estimation analysis in extracting meaningful insights from discrete choice models. While constructing and estimating a model is an essential step, the true value of a DCE-based model lies in interpreting and applying its results. This chapter demonstrates how to translate model outputs, such as marginal willingness to pay and changes in consumer surplus, into practical, policy-relevant insights that enhance decision-making. We explore issues relevant to these outputs and provide guidance on how to accurately report uncertainty in your results.

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Post-Estimation Analysis

  • 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 highlights the importance of the post-estimation analysis in extracting meaningful insights from discrete choice models. While constructing and estimating a model is an essential step, the true value of a DCE-based model lies in interpreting and applying its results. This chapter demonstrates how to translate model outputs, such as marginal willingness to pay and changes in consumer surplus, into practical, policy-relevant insights that enhance decision-making. We explore issues relevant to these outputs and provide guidance on how to accurately report uncertainty in your results.