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Differences Between Classical and Bayesian Estimates for Mixed Logit Models: A Replication Study

Author

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  • Ossama Elshiewy
  • German Zenetti
  • Yasemin Boztug

Abstract

The mixed logit model is widely used in applied econometrics. Researchers typically rely on the free choice between the classical and Bayesian estimation approach. However, empirical evidence of the similarity of their parameter estimates is sparse. The presumed similarity is mainly based on one empirical study that analyzes a single dataset (Huber J, Train KE. 2001. On the similarity of classical and Bayesian estimates of individual mean partworths. Marketing Letters12(3): 259–269). Our replication study offers a generalization of their results by comparing classical and Bayesian parameter estimates from six additional datasets and specifically for panel versus cross‐sectional data. In general, our results suggest that the two methods provide similar results, with less similarity for cross‐sectional data than for panel data. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Ossama Elshiewy & German Zenetti & Yasemin Boztug, 2017. "Differences Between Classical and Bayesian Estimates for Mixed Logit Models: A Replication Study," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 470-476, March.
    • Handle: RePEc:wly:japmet:v:32:y:2017:i:2:p:470-476
    DOI: 10.1002/jae.2513
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    Cited by:

    1. Pleshcheva, Vlada, 2019. "Metric and Scale Effects in Consumer Preferences for Environmental Benefits," Rationality and Competition Discussion Paper Series 147, CRC TRR 190 Rationality and Competition.
    2. Kassie, Girma T. & Zeleke, Fresenbet & Birhanu, Mulugeta Y. & Scarpa, Riccardo, 2020. "Reminder Nudge, Attribute Nonattendance, and Willingness to Pay in a Discrete Choice Experiment," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304208, Agricultural and Applied Economics Association.
    3. Akinc, Deniz & Vandebroek, Martina, 2018. "Bayesian estimation of mixed logit models: Selecting an appropriate prior for the covariance matrix," Journal of choice modelling, Elsevier, vol. 29(C), pages 133-151.
    4. Woo, JongRoul & Shin, Jungwoo & Kim, Hongbum & Moon, HyungBin, 2022. "Which consumers are willing to pay for smart car healthcare services? A discrete choice experiment approach," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    5. Emily Lancsar & Denzil G. Fiebig & Arne Risa Hole, 2017. "Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software," PharmacoEconomics, Springer, vol. 35(7), pages 697-716, July.

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