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mlogit: Random Utility Models in R

Author

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  • Yves Croissant

    (CEMOI - Centre d'Économie et de Management de l'Océan Indien - UR - Université de La Réunion)

Abstract

mlogit is a package for R which enables the estimation of random utility models with choice situation and/or alternative specific variables. The main extensions of the basic multinomial model (heteroscedastic, nested and random parameter models) are implemented.

Suggested Citation

  • Yves Croissant, 2020. "mlogit: Random Utility Models in R," Post-Print hal-03019603, HAL.
    • Handle: RePEc:hal:journl:hal-03019603
    DOI: 10.18637/jss.v095.i11
    Note: View the original document on HAL open archive server: https://hal.univ-reunion.fr/hal-03019603
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    References listed on IDEAS

    as
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    2. Hasan, Asad & Wang, Zhiyu & Mahani, Alireza S., 2016. "Fast Estimation of Multinomial Logit Models: R Package mnlogit," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i03).
    3. Ben-Akiva, M. & Bolduc, D. & Bradley, M., 1993. "Estimation of Travel Choice Models with Randomly Distributed Values of Time," Papers 9303, Laval - Recherche en Energie.
    4. 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.
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    2. Marine Albert & Jacques-Eric Bergez & Magali Willaume & Stéphane Couture, 2022. "Vulnerability of Maize Farming Systems to Climate Change: Farmers’ Opinions Differ about the Relevance of Adaptation Strategies," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    3. Greene, Claire & Prescott, Brian & Shy, Oz, 2022. "How people pay each other: Data, theory, and calibrations," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 96(C).
    4. Shr, Yau-Huo Jimmy & Zhang, Wendong, 2021. "Does Omitting Downstream Water Quality Change the Economic Benefits of Nutrient Reduction Programs: Evidence from a Discrete Choice Experiment," 2021 Annual Meeting, August 1-3, Austin, Texas 313927, Agricultural and Applied Economics Association.
    5. Haoge Chang & Yusuke Narita & Kota Saito, 2022. "Approximating Choice Data by Discrete Choice Models," Papers 2205.01882, arXiv.org, revised Dec 2023.
    6. Yau-Huo Shr & Wendong Zhang, 2021. "Does Omitting Downstream Water Quality Change the Economic Benefits of Nutrient Reduction? Evidence from a Discrete Choice Experiment," Center for Agricultural and Rural Development (CARD) Publications 21-wp620, Center for Agricultural and Rural Development (CARD) at Iowa State University.

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    Keywords

    discrete choice models; maximum likelihood estimation; R; econometrics;
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