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Modelling preference heterogeneity using a Bayesian finite mixture of Almost Ideal Demand Systems

Author

Listed:
  • Ariane Kehlbacher
  • Chittur Srinivasan
  • Rachel McCloy
  • Richard Tiffin

Abstract

Demand studies often use observable characteristics to proxy preference heterogeneity. It is likely, however, that some households with the same observable characteristics have quite different preferences. An alternative approach is to use a Gaussian mixture of Almost Ideal Demand Systems to capture the heterogeneity. We show how to estimate this with censored purchase data for 5 food categories using Bayesian inference. Using model outputs we infer four different preference classes; how distinct these classes are from one another and which food categories are driving the segmentation process.

Suggested Citation

  • Ariane Kehlbacher & Chittur Srinivasan & Rachel McCloy & Richard Tiffin, 2020. "Modelling preference heterogeneity using a Bayesian finite mixture of Almost Ideal Demand Systems," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(3), pages 933-970.
    • Handle: RePEc:oup:erevae:v:47:y:2020:i:3:p:933-970.
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    File URL: http://hdl.handle.net/10.1093/erae/jbz002
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    Cited by:

    1. Liana Jacobi & Nhung Nghiem & Andrés Ramírez‐Hassan & Tony Blakely, 2021. "Food Price Elasticities for Policy Interventions: Estimates from a Virtual Supermarket Experiment in a Multistage Demand Analysis with (Expert) Prior Information," The Economic Record, The Economic Society of Australia, vol. 97(319), pages 457-490, December.
    2. Andrés Ramírez‐Hassan, 2021. "Bayesian estimation of the exact affine Stone index demand system: Replicating the Lewbel and Pendakur (2009) results," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(4), pages 484-491, June.

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