A Bayesian mixed logit–probit model for multinomial choice
用于多项选择的贝叶斯混合 Logit–Probit 模型AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究多项离散选择，并使用持续二十四个月的家庭超市选择面板。
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用于多项选择的贝叶斯混合 Logit–Probit 模型
- 研究问题
- 如何在多项离散选择中灵活刻画个体偏好的多峰分布，同时放松个体层面的无关替代项独立性，并处理家庭的重复选择记录？
- 方法与思路
- 作者采用层级混合模型，对关键系数设置多元 Dirichlet 过程先验，其他系数服从多元正态，并用潜在类别 MCMC 抽样。
- 材料陈述的贡献
- 摘要称，半参数模型在休斯敦家庭超市选择数据中识别出复杂的多峰偏好分布，区分重视低价、便利性及边际内消费者。
- 阅读关注点
- 应核对不同系数分布如何共同决定选择概率、面板内相关如何处理，以及先验、抽样收敛和偏好多峰识别的依据。
- 阅读边界
- 仅据所给 RePEc 出版社摘要解读；所谓无假设密度设定仍需结合先验理解，摘要未说明完整识别条件或后验敏感性。
原文摘要
In this paper, we introduce a new flexible mixed model for multinomial discrete choice where the key individual- and alternative-specific parameters of interest are allowed to follow an assumption-free nonparametric density specification, while other alternative-specific coefficients are assumed to be drawn from a multivariate Normal distribution, which eliminates the independence of irrelevant alternatives assumption at the individual level. A hierarchical specification of our model allows us to break down a complex data structure into a set of submodels with the desired features that are naturally assembled in the original system. We estimate the model, using a Bayesian Markov Chain Monte Carlo technique with a multivariate Dirichlet Process (DP) prior on the coefficients with nonparametrically estimated density. We employ a "latent class" sampling algorithm, which is applicable to a general class of models, including non-conjugate DP base priors. The model is applied to supermarket choices of a panel of Houston households whose shopping behavior was observed over a 24-month period in years 2004-2005. We estimate the nonparametric density of two key variables of interest: the price of a basket of goods based on scanner data, and driving distance to the supermarket based on their respective locations. Our semi-parametric approach allows us to identify a complex multi-modal preference distribution, which distinguishes between inframarginal consumers and consumers who strongly value either lower prices or shopping convenience.