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An empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications

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  • Hruschka, Harald
  • Fettes, Werner
  • Probst, Markus

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  • Hruschka, Harald & Fettes, Werner & Probst, Markus, 2004. "An empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications," European Journal of Operational Research, Elsevier, vol. 159(1), pages 166-180, November.
  • Handle: RePEc:eee:ejores:v:159:y:2004:i:1:p:166-180
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    1. Abe, Makoto, 1999. "A Generalized Additive Model for Discrete-Choice Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 271-284, July.
    2. McFadden, Daniel, 1980. "Econometric Models for Probabilistic Choice among Products," The Journal of Business, University of Chicago Press, vol. 53(3), pages 13-29, July.
    3. Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, vol. 16(4), pages 370-391.
    4. Marcel L. Corstjens & David A. Gautschi, 1983. "Formal Choice Models in Marketing," Marketing Science, INFORMS, vol. 2(1), pages 19-56.
    5. Yves Bentz & Dwight Merunka, 2000. "Neural networks and the multinomial logit for brand choice modelling: a hybrid approach," Post-Print hal-01822273, HAL.
    6. Pradeep K. Chintagunta, 1992. "Estimating a Multinomial Probit Model of Brand Choice Using the Method of Simulated Moments," Marketing Science, INFORMS, vol. 11(4), pages 386-407.
    7. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    8. Kalyanaram, Gurumurthy & Little, John D C, 1994. "An Empirical Analysis of Latitude of Price Acceptance in Consumer Package Goods," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(3), pages 408-418, December.
    9. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    10. Kwangpil Chang & S. Siddarth & Charles B. Weinberg, 1999. "The Impact of Heterogeneity in Purchase Timing and Price Responsiveness on Estimates of Sticker Shock Effects," Marketing Science, INFORMS, vol. 18(2), pages 178-192.
    11. Thomas S. Shively & Greg M. Allenby & Robert Kohn, 2000. "A Nonparametric Approach to Identifying Latent Relationships in Hierarchical Models," Marketing Science, INFORMS, vol. 19(2), pages 149-162, November.
    12. Allenby, Greg M & Lenk, Peter J, 1995. "Reassessing Brand Loyalty, Price Sensitivity, and Merchandising Effects on Consumer Brand Choice," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 281-289, July.
    13. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    14. James H. Pedrick & Fred S. Zufryden, 1991. "Evaluating the Impact of Advertising Media Plans: A Model of Consumer Purchase Dynamics Using Single-Source Data," Marketing Science, INFORMS, vol. 10(2), pages 111-130.
    15. Lakshman Krishnamurthi & S. P. Raj, 1988. "A Model of Brand Choice and Purchase Quantity Price Sensitivities," Marketing Science, INFORMS, vol. 7(1), pages 1-20.
    16. David R. Bell & James M. Lattin, 2000. "Looking for Loss Aversion in Scanner Panel Data: The Confounding Effect of Price Response Heterogeneity," Marketing Science, INFORMS, vol. 19(2), pages 185-200, May.
    17. Winer, Russell S, 1986. "A Reference Price Model of Brand Choice for Frequently Purchased Products," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(2), pages 250-256, September.
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    Cited by:

    1. Arkoudi, Ioanna & Krueger, Rico & Azevedo, Carlos Lima & Pereira, Francisco C., 2023. "Combining discrete choice models and neural networks through embeddings: Formulation, interpretability and performance," Transportation Research Part B: Methodological, Elsevier, vol. 175(C).
    2. Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
    3. Harald Hruschka, 2007. "Using a heterogeneous multinomial probit model with a neural net extension to model brand choice," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 113-127.
    4. Yafei Han & Francisco Camara Pereira & Moshe Ben-Akiva & Christopher Zegras, 2020. "A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability," Papers 2002.00922, arXiv.org, revised Jul 2022.
    5. Lorena Torres Lahoz & Francisco Camara Pereira & Georges Sfeir & Ioanna Arkoudi & Mayara Moraes Monteiro & Carlos Lima Azevedo, 2023. "Attitudes and Latent Class Choice Models using Machine learning," Papers 2302.09871, arXiv.org.
    6. Lang, Stefan & Steiner, Winfried J. & Weber, Anett & Wechselberger, Peter, 2015. "Accommodating heterogeneity and nonlinearity in price effects for predicting brand sales and profits," European Journal of Operational Research, Elsevier, vol. 246(1), pages 232-241.
    7. Sifringer, Brian & Lurkin, Virginie & Alahi, Alexandre, 2020. "Enhancing discrete choice models with representation learning," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 236-261.
    8. Han, Yafei & Pereira, Francisco Camara & Ben-Akiva, Moshe & Zegras, Christopher, 2022. "A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 166-186.

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