随机系数模型内生性的控制函数校正:基于选择的推荐系统

IF 2.8 3区 经济学 Q1 ECONOMICS Journal of Choice Modelling Pub Date : 2023-03-01 DOI:10.1016/j.jocm.2022.100399
Mazen Danaf , C. Angelo Guevara , Moshe Ben-Akiva
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引用次数: 0

摘要

离散选择模型在个性化中的应用在研究人员和从业者中越来越受欢迎。然而,在这样的系统中,当向用户呈现连续的菜单(或选择情况)时,每个菜单中的选项和属性取决于用户在先前菜单中做出的选择。这就产生了内生性,可能导致不一致的估计。我们的配套论文Danaf等人(2020)表明,只有当每个用户的整个选择历史都包含在估计中时,估计才是一致的。然而,由于计算限制或数据可用性的原因,这可能不可行。在本文中,我们提出了一种控制函数(CF)校正,用于估计中不能包括选择历史的情况。我们的方法使用非个性化属性的属性作为工具,并通过包括解释变量和第一阶段残差之间的相互作用来应用CF校正。估计可以按顺序进行,也可以同时进行,但后者更有效(如果模型反映了真实的数据生成过程)。该方法能够恢复分布系数的总体均值,特别是在具有较长选择历史的情况下。方差被低估了,因为消费者之间的部分可变性是由残差解释的,残差包含在系统效用中。然而,可以根据估计结果来计算总体方差。修正后的效用方程(包括残差)可用于预测和模型应用,并提供优越的拟合和预测。
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A control-function correction for endogeneity in random coefficients models: The case of choice-based recommender systems

Applications of discrete choice models in personalization are becoming increasingly popular among researchers and practitioners. However, in such systems, when users are presented with successive menus (or choice situations), the alternatives and attributes in each menu depend on the choices made by the user in the previous menus. This gives rise to endogeneity which can result in inconsistent estimates. Our companion paper, Danaf et al. (2020), showed that the estimates are only consistent when the entire choice history of each user is included in estimation. However, this might not be feasible because of computational constraints or data availability. In this paper, we present a control-function (CF) correction for the cases where the choice history cannot be included in estimation. Our method uses the attributes of non-personalized attributes as instruments, and applies the CF correction by including interactions between the explanatory variables and the first stage residuals. Estimation can be done either sequentially or simultaneously, however, the latter is more efficient (if the model reflects the true data generating process). This method is able to recover the population means of the distributed coefficients, especially with a long choice history. The variances are underestimated, because part of the inter-consumer variability is explained by the residuals, which are included in the systematic utility. However, the population variances can be computed from the estimation results. The modified utility equations (which include the residuals) can be used in forecasting and model application, and provide superior fit and predictions.

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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
期刊最新文献
Editorial Board Latent class choice models with an error structure: Investigating potential unobserved associations between latent segmentation and behavior generation Model choice and framing effects: Do discrete choice modeling decisions affect loss aversion estimates? A consistent moment equations for binary probit models with endogenous variables using instrumental variables Transformation-based flexible error structures for choice modeling
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