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Logit Mixed Logit Under Asymmetry and Multimodality of WTP: A Monte Carlo Evaluation

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  • Riccardo Scarpa
  • Cristiano Franceschinis
  • Mara Thiene

Abstract

The logit‐mixed logit (LML) model advances choice modeling by generalizing previous parametric and semi‐nonparametric specifications and allowing retrieval of flexible taste distributions. Using standard operating conditions in the field, we report results from Monte Carlo experiments designed to assess the finite sample bias‐variance tradeoff for the LML using as a benchmark conventional Mixed logit models (MXL) under asymmetric and multimodal taste distributions. The LML specification always outperforms the MXL in terms of bias, but when the variance around modes is high the mean squared error (MSE) is lower than that of MXL only at sample sizes larger than usual and with some nuances. D‐error minimizing experimental design predicated on multinomial logit significantly reduces MSE, but no clear winner is found between polynomial, step, and spline functions for the multidimensional grid function. Analysis of empirical data from a choice experiment on tap water shows that multimodality emerges only if higher number of node parameters are used in the LML.

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  • Riccardo Scarpa & Cristiano Franceschinis & Mara Thiene, 2021. "Logit Mixed Logit Under Asymmetry and Multimodality of WTP: A Monte Carlo Evaluation," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 643-662, March.
    • Handle: RePEc:wly:ajagec:v:103:y:2021:i:2:p:643-662
    DOI: 10.1111/ajae.12122
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    1. Claudia Bazzani & Marco A. Palma & Rodolfo M. Nayga, 2018. "On the use of flexible mixing distributions in WTP space: an induced value choice experiment," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 62(2), pages 185-198, April.
    2. Yuxin Chen & Ganesh Iyer, 2002. "Research Note Consumer Addressability and Customized Pricing," Marketing Science, INFORMS, vol. 21(2), pages 197-208, November.
    3. David Hensher & Nina Shore & Kenneth Train, 2005. "Households’ Willingness to Pay for Water Service Attributes," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 32(4), pages 509-531, December.
    4. Fosgerau, Mogens & Hess, Stephane, 2009. "A comparison of methods for representing random taste heterogeneity in discrete choice models," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 42, pages 1-25.
    5. Paul Belleflamme & Wing Man Wynne Lam & Wouter Vergote, 2017. "Price Discrimination and Dispersion under Asymmetric Profiling of Consumers," Working Papers halshs-01502452, HAL.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    7. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    8. Bansal, Prateek & Daziano, Ricardo A & Guerra, Erick, 2018. "Minorization-Maximization (MM) algorithms for semiparametric logit models: Bottlenecks, extensions, and comparisons," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 17-40.
    9. Jeffrey M. Wooldridge, 2011. "Fractional response models with endogeneous explanatory variables and heterogeneity," CHI11 Stata Conference 12, Stata Users Group.
    10. Mara Thiene & Riccardo Scarpa & Jordan Louviere, 2015. "Addressing Preference Heterogeneity, Multiple Scales and Attribute Attendance with a Correlated Finite Mixing Model of Tap Water Choice," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(3), pages 637-656, November.
    11. Bansal, Prateek & Daziano, Ricardo A. & Achtnicht, Martin, 2018. "Extending the logit-mixed logit model for a combination of random and fixed parameters," Journal of choice modelling, Elsevier, vol. 27(C), pages 88-96.
    12. Ferrini, Silvia & Scarpa, Riccardo, 2007. "Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study," Journal of Environmental Economics and Management, Elsevier, vol. 53(3), pages 342-363, May.
    13. Riccardo Scarpa & Mara Thiene & Francesco Marangon, 2008. "Using Flexible Taste Distributions to Value Collective Reputation for Environmentally Friendly Production Methods," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 56(2), pages 145-162, June.
    14. Bansal, Prateek & Daziano, Ricardo A. & Achtnicht, Martin, 2018. "Comparison of parametric and semiparametric representations of unobserved preference heterogeneity in logit models," Journal of choice modelling, Elsevier, vol. 27(C), pages 97-113.
    15. Rungie, Cam & Scarpa, Riccardo & Thiene, Mara, 2014. "The influence of individuals in forming collective household preferences for water quality," Journal of Environmental Economics and Management, Elsevier, vol. 68(1), pages 161-174.
    16. Riccardo Scarpa & Kenneth G. Willis & Melinda Acutt, 2007. "Valuing externalities from water supply: Status quo, choice complexity and individual random effects in panel kernel logit analysis of choice experiments," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 50(4), pages 449-466.
    17. Riccardo Scarpa & Cristiano Franceschinis & Mara Thiene, 2017. "A Monte Carlo Evaluation of the Logit-Mixed Logit under Asymmetry and Multimodality," Working Papers in Economics 17/23, University of Waikato.
    18. Train, Kenneth, 2016. "Mixed logit with a flexible mixing distribution," Journal of choice modelling, Elsevier, vol. 19(C), pages 40-53.
    19. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    20. Andrew Daly & Stephane Hess & Kenneth Train, 2012. "Assuring finite moments for willingness to pay in random coefficient models," Transportation, Springer, vol. 39(1), pages 19-31, January.
    21. Caputo, Vincenzina & Scarpa, Riccardo & Nayga, Rodolfo M. & Ortega, David L., 2018. "Are preferences for food quality attributes really normally distributed? An analysis using flexible mixing distributions," Journal of choice modelling, Elsevier, vol. 28(C), pages 10-27.
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    5. Romain Crastes dit Sourd, 2024. "A new empirical approach for mitigating exploding implicit prices in mixed multinomial logit models," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(1), pages 76-95, January.

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