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Estimation of Discrete Choice Models with Many Alternatives Using Random Subsets of the Full Choice Set: With an Application to Demand for Frozen Pizza

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  • Nada Wasi

    (University of Michigan, USA)

  • Michael P. Keane

    (Nuffield College, University of Oxford, UK)

Abstract

A common problem in estimation of discrete choice models is that the complete choice set is very large. A good example is supermarket consumer goods, like breakfast cereal, where there are often a hundred or more varieties (SKUs or UPCs) to choose from. In that case, estimation of complex discrete choice models where choice probabilities have no closed form can be very computationally burdensome. We show how use of random subsets of the full choice set can be a useful device to reduce computational burden. We apply this approach to estimating demand for frozen pizza, where there are nearly 100 varieties to choose from. We provide some interesting new results on how price changes for a particular variety of a brand lead to variety switching within the brand vs. brand switching. In particular, when a variety raises its price, most switching is to other brands, rather than to other varieties of the same brand.

Suggested Citation

  • Nada Wasi & Michael P. Keane, 2012. "Estimation of Discrete Choice Models with Many Alternatives Using Random Subsets of the Full Choice Set: With an Application to Demand for Frozen Pizza," Economics Papers 2012-W13, Economics Group, Nuffield College, University of Oxford.
  • Handle: RePEc:nuf:econwp:1213
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    File URL: http://www.nuffield.ox.ac.uk/economics/papers/2012/RandomChoice.pdf
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    References listed on IDEAS

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    2. Hellali, Wajdi & Kallas, Zein & Gil, José María, 2015. "Consumer’s revealed preferences for yogurt purchase in Catalonia: A Generalized Multinomial Logit Approach," 148th Seminar, November 30-December 1, 2015, The Hague, The Netherlands 229263, European Association of Agricultural Economists.
    3. Beck, Günter W. & Kotz, Hans-Helmut & Zabelina, Natalia, 2016. "Prices and consumer purchasing preferences at the border: Evidence from a multi-country household scanner data set," CFS Working Paper Series 536, Center for Financial Studies (CFS).
    4. Youyi Bi & Yunjian Qiu & Zhenghui Sha & Mingxian Wang & Yan Fu & Noshir Contractor & Wei Chen, 2021. "Modeling Multi-Year Customers’ Considerations and Choices in China’s Auto Market Using Two-Stage Bipartite Network Analysis," Networks and Spatial Economics, Springer, vol. 21(2), pages 365-385, June.
    5. Crawford, Gregory S. & Griffith, Rachel & Iaria, Alessandro, 2021. "A survey of preference estimation with unobserved choice set heterogeneity," Journal of Econometrics, Elsevier, vol. 222(1), pages 4-43.
    6. Fiona Steele & Elizabeth Washbrook & Christopher Charlton & William J. Browne, 2016. "A Longitudinal Mixed Logit Model for Estimation of Push and Pull Effects in Residential Location Choice," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1061-1074, July.
    7. Michael P. Keane & Susan Thorp, 2016. "Complex Decision Making: The Roles of Cognitive Limitations, Cognitive Decline and Ageing," Economics Papers 2016-W10, Economics Group, Nuffield College, University of Oxford.
    8. Murwirapachena, Genius & Dikgang, Johane, 2018. "An empirical examination of reducing status quo bias in heterogeneous populations: evidence from the South African water sector," MPRA Paper 91549, University Library of Munich, Germany.
    9. Romana Khan & Ting Zhu & Sanjay Dhar, 2018. "The effect of the WIC program on consumption patterns in the cereal category," Quantitative Marketing and Economics (QME), Springer, vol. 16(1), pages 79-109, March.
    10. Jonathan James, 2018. "Estimation of Factor Structured Covariance Mixed Logit Models," Working Papers 1802, California Polytechnic State University, Department of Economics.
    11. James, Jonathan, 2018. "Estimation of factor structured covariance mixed logit models," Journal of choice modelling, Elsevier, vol. 28(C), pages 41-55.

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