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首页> 外文期刊>MANAGEMENT SCIENCE >On the Recoverability of Choice Behaviors with Random Coefficients Choice Models in the Context of Limited Data and Unobserved Effects
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On the Recoverability of Choice Behaviors with Random Coefficients Choice Models in the Context of Limited Data and Unobserved Effects

机译：数据有限和观测不到的情况下具有随机系数选择模型的选择行为的可恢复性

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摘要

Random coefficients choice models are seeing widespread adoption in marketing research, partly because of their ability to generate household-level parameter estimates with limited data. However, the power of such models may tempt researchers to trust that they continue to produce reasonable estimates, when in fact either model misspecification or insufficient data limits the models' ability to recover household-level parameters successfully. If household-level choice behaviors are not recovered successfully, managerial decisions such as marketing-mix planning and targeting, direct marketing, segmentation, and forecasting may not produce the desired results. This study addresses the following questions. First, can random coefficients choice models correctly identify markets characterized by preference and response heterogeneity, state dependence, the use of alternative decision heuristics that result in reduced choice sets, and combinations of these effects? If so, how much data is required, and is this realistic given the size of data sets typically used in marketing analyses? Which model selection criteria should be used to identify these markets? When there is spurious market identification, which parameters contribute to the spurious result? An extensive simulation experiment is conducted wherein random coefficients logit models with varying specifications of parameter heterogeneity, state dependence effects, and choice set heterogeneity are applied to 128 experimental conditions. The results show which types of markets can be identified reliably and which cannot. Based on the results of the simulation, the authors develop a model selection heuristic that identifies the correct market in 81% of the experimental conditions. In contrast, strict application of the best model selection criterion alone results in correct market identification in at most 34% of experimental conditions. Interestingly, we find that the amount of data (number of households or number of purchases per household) does not affect our ability to identify the correct market type with this heuristic, so there is a good chance of identifying the correct market type even when little data is available.
机译：随机系数选择模型已在市场研究中得到广泛采用，部分原因是它们具有生成有限数据的家庭级参数估计值的能力。但是，这种模型的强大功能可能会诱使研究人员相信它们会继续产生合理的估计，而实际上，模型错误指定或数据不足会限制模型成功恢复家庭水平参数的能力。如果未能成功恢复家庭层面的选择行为，则诸如营销组合计划和目标定位，直接营销，细分和预测之类的管理决策可能不会产生预期的结果。本研究解决以下问题。首先，随机系数选择模型能否正确地识别以偏好和响应异质性，状态依赖性，使用导致选择集减少的替代决策启发法以及这些效应的组合为特征的市场？如果是这样，则需要多少数据，考虑到营销分析中通常使用的数据集的大小，这是否现实？应该使用哪种模型选择标准来识别这些市场？当存在虚假市场识别时，哪些参数会导致虚假结果？进行了广泛的仿真实验，其中将具有参数异质性，状态依赖效应和选择集异质性不同规格的随机系数logit模型应用于128个实验条件。结果表明，哪些类型的市场可以可靠地识别，哪些不能。基于仿真结果，作者开发了一种模型选择启发式方法，可以在81％的实验条件下确定正确的市场。相反，仅严格应用最佳模型选择标准才能在多达34％的实验条件下正确识别市场。有趣的是，我们发现数据量（家庭数量或每个家庭的购买数量）不会影响我们通过这种启发式方法识别正确的市场类型的能力，因此即使很少，也很有可能识别正确的市场类型。数据可用。

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