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Model selection and misspecification in discrete choice welfare analysis

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  • Ju-Chin Huang
  • Min Qiang Zhao

Abstract

This study extends the work by Herriges and Kling (1997) to further evaluate the impact of discrete choice modelling techniques on welfare measures. Particularly, we evaluate the performance of the increasingly popular mixed logit model and the computational strategy for deriving discrete choice welfare measures. Our simulation results show that model misspecification can have profound effects on welfare measures. In general, the flexible mixed logit model performs relatively well in the presence of misspecification. However, when the nesting structure can be appropriately identified (via statistical tests and a priori knowledge/experience), the nested logit model provides more reliable welfare measures than the mixed logit model.

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  • Ju-Chin Huang & Min Qiang Zhao, 2015. "Model selection and misspecification in discrete choice welfare analysis," Applied Economics, Taylor & Francis Journals, vol. 47(39), pages 4153-4167, August.
    • Handle: RePEc:taf:applec:v:47:y:2015:i:39:p:4153-4167
    DOI: 10.1080/00036846.2015.1026581
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    Cited by:

    1. Muhammad Bello & Awudu Abdulai, 2016. "Measuring heterogeneity, survey engagement and response quality in preferences for organic products in Nigeria," Applied Economics, Taylor & Francis Journals, vol. 48(13), pages 1159-1171, March.

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