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A simple method for estimating preference parameters for individuals

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  • Frischknecht, Bart D.
  • Eckert, Christine
  • Geweke, John
  • Louviere, Jordan J.

Abstract

This paper demonstrates a method for estimating logit choice models for small sample data, including single individuals, that is computationally simpler and relies on weaker prior distributional assumptions compared to hierarchical Bayes estimation. Using Monte Carlo simulations and online discrete choice experiments, we show how this method is particularly well suited to estimating values of choice model parameters from small sample choice data, thus opening this area to the application of choice modeling. For larger sample sizes of approximately 100–200 respondents, preference distribution recovery is similar to hierarchical Bayes estimation of mixed logit models for the examples we demonstrate. We discuss three approaches for specifying the conjugate priors required for the method: specifying priors based on existing or projected market shares of products, specifying a flat prior on the choice alternatives in a discrete choice experiment, or adopting an empirical Bayes approach where the prior choice probabilities are taken to be the average choice probabilities observed in a discrete choice experiment. We show that for small sample data, the relative weighting of the prior during estimation is an important consideration, and we present an automated method for selecting the weight based on a predictive scoring rule.

Suggested Citation

  • Frischknecht, Bart D. & Eckert, Christine & Geweke, John & Louviere, Jordan J., 2014. "A simple method for estimating preference parameters for individuals," International Journal of Research in Marketing, Elsevier, vol. 31(1), pages 35-48.
    • Handle: RePEc:eee:ijrema:v:31:y:2014:i:1:p:35-48
    DOI: 10.1016/j.ijresmar.2013.07.005
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    3. Tran, Yen & Yamamoto, Toshiyuki & Sato, Hitomi & Miwa, Tomio & Morikawa, Takayuki, 2020. "The analysis of influences of attitudes on mode choice under highly unbalanced mode share patterns," Journal of choice modelling, Elsevier, vol. 36(C).
    4. Kessels, Roselinde & Jones, Bradley & Goos, Peter, 2019. "Using Firth's method for model estimation and market segmentation based on choice data," Journal of choice modelling, Elsevier, vol. 31(C), pages 1-21.
    5. Tran, Yen & Yamamoto, Toshiyuki & Sato, Hitomi, 2020. "The influences of environmentalism and attitude towards physical activity on mode choice: The new evidences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 211-226.
    6. Narine Yegoryan & Daniel Guhl & Friederike Paetz, 2023. "When Zeros Count: Confounding in Preference Heterogeneity and Attribute Non-attendance," Rationality and Competition Discussion Paper Series 482, CRC TRR 190 Rationality and Competition.
    7. Hazel Bateman & Christine Eckert & Fedor Iskhakov & Jordan Louviere & Stephen Satchell & Susan Thorp, 2017. "Default and naive diversification heuristics in annuity choice," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 32-57, February.

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