Modelling preference heterogeneity using a Bayesian finite mixture of Almost Ideal Demand Systems
Modelling preference heterogeneity using a Bayesian finite mixture of Almost Ideal Demand Systems
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DOI：
10.1093/erae/jbz002
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发表时间：
2020-06
影响因子：
3.4
通讯作者：
Ariane Kehlbacher;C. Srinivasan;R. McCloy;R. Tiffin
中科院分区：
文献类型：
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作者：
Ariane Kehlbacher;C. Srinivasan;R. McCloy;R. Tiffin
需求研究经常使用可观察到的特征来代表偏好异质性。然而，一些具有相同可观察特征的家庭可能有相当不同的偏好。另一种方法是使用几乎理想的需求系统的高斯混合来捕捉异质性。我们展示了如何使用贝叶斯推断在5类食品的删失购买数据下估计这一点。使用模型输出，我们推断出四个不同的偏好类别；这些类别彼此之间的差异有多大，以及哪些食品类别正在推动细分过程。
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.