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A model for multiple brand choice

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  • Baltas, George

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  • Baltas, George, 2004. "A model for multiple brand choice," European Journal of Operational Research, Elsevier, vol. 154(1), pages 144-149, April.
    • Handle: RePEc:eee:ejores:v:154:y:2004:i:1:p:144-149
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    1. Kimhi, Ayal, 1999. "On the Tradeoff between Computational Simplicity and Asymptotic Properties in Multivariate Probit," Computational Economics, Springer;Society for Computational Economics, vol. 13(1), pages 93-101, February.
    2. McFadden, Daniel & Ruud, Paul A, 1994. "Estimation by Simulation," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 591-608, November.
    3. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
    4. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    5. Baourakis, G. & Matsatsinis, N. F. & Siskos, Y., 1996. "Agricultural product development using multidimensional and multicriteria analyses: The case of wine," European Journal of Operational Research, Elsevier, vol. 94(2), pages 321-334, October.
    6. Manrai, Ajay K., 1995. "Mathematical models of brand choice behavior," European Journal of Operational Research, Elsevier, vol. 82(1), pages 1-17, April.
    7. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    8. George Baltas, 2002. "An applied analysis of brand demand structure," Applied Economics, Taylor & Francis Journals, vol. 34(9), pages 1171-1175.
    9. G Baltas & P Doyle, 1998. "An empirical analysis of private brand demand recognising heterogeneous preferences and choice dynamics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 49(8), pages 790-798, August.
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    1. Xiaoping Li & Yan Yan & Liuyang Yao, 2020. "‘Get a Fish’ vs. ‘Get a Fishing Skill’: Farmers’ Preferred Compensation Methods to Control Agricultural Nonpoint Source Pollution," IJERPH, MDPI, vol. 17(7), pages 1-13, April.
    2. Luo, Zheng & Chen, Xu & Chen, Jing & Wang, Xiaojun, 2017. "Optimal pricing policies for differentiated brands under different supply chain power structures," European Journal of Operational Research, Elsevier, vol. 259(2), pages 437-451.
    3. Taha Hossein Rashidi & Matthew J. Roorda, 2018. "A business establishment fleet ownership and composition model," Transportation, Springer, vol. 45(3), pages 971-987, May.
    4. Jae Young Choi & Yeonbae Kim & Yungman Jun & Yunhee Kim, 2009. "A Multivariate Probit Analysis of Korean Firms Information System Adoption: An Empirical Analysis on the Determinants of the Adoption and Complementarity Among the Information Systems," TEMEP Discussion Papers 200926, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Nov 2009.
    5. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    6. Castro, Marisol & Bhat, Chandra R. & Pendyala, Ram M. & Jara-Díaz, Sergio R., 2012. "Accommodating multiple constraints in the multiple discrete–continuous extreme value (MDCEV) choice model," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 729-743.
    7. Bhat, Chandra R. & Srinivasan, Sivaramakrishnan & Sen, Sudeshna, 2006. "A joint model for the perfect and imperfect substitute goods case: Application to activity time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 40(10), pages 827-850, December.
    8. Kalouptsidis, N. & Koutroumbas, K. & Psaraki, V., 2007. "Classification methods for random utility models with i.i.d. disturbances under the most probable alternative rule," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1778-1794, February.
    9. Pancras, Joseph, 2011. "The nested consideration model: Investigating dynamic store consideration sets and store competition," European Journal of Operational Research, Elsevier, vol. 214(2), pages 340-347, October.
    10. Yanan Yu & Yong He & Xuan Zhao & Li Zhou, 2022. "Certify or not? An analysis of organic food supply chain with competing suppliers," Annals of Operations Research, Springer, vol. 314(2), pages 645-675, July.
    11. Jungwoo Shin & Chang Seob Kimi & Jongsu Lee, 2009. "Model for Studying Commodity Bundling with a Focus on Consumer Preference," TEMEP Discussion Papers 200934, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Nov 2009.
    12. He, X. & Reiner, D., 2018. "Consumer Engagement in Energy Markets: The Role of Information and Knowledge," Cambridge Working Papers in Economics 1867, Faculty of Economics, University of Cambridge.
    13. Koo, Yoonmo & Lim, Sesil & Kim, Kayoung & Cho, Youngsang, 2014. "Analysis of user characteristics regarding social network services in South Korea using the multivariate probit model," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 232-240.
    14. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.

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