Assessing consumer demand with noisy neural measurements
Assessing consumer demand with noisy neural measurements
复制标题
通过噪声神经测量评估消费者需求
DOI：
10.1016/j.jeconom.2020.07.028
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发表时间：
2021
影响因子：
6.3
通讯作者：
Levy, Ifat
中科院分区：
文献类型：
--
作者：
Webb, Ryan;Mehta, Nitin;Levy, Ifat
最近的研究使用随机效用框架来研究神经数据是否可以评估和预测个人内部和跨个人的消费品需求。然而，这种方法的有效性受到神经数据中较大程度的测量误差的限制。由此产生的“变量误差”问题严重影响了神经测量和选择行为之间关系的估计，从而限制了这些数据在评估效用边际贡献方面的作用。在这篇文章中，我们提出了一种方法来控制这种大程度的测量误差的大脑的值区域。我们建议，额外的神经变量从大脑区域无关的估值可以作为“代理”的测量误差的价值区域，大大减轻模型估计的偏差。我们证明了我们提出的方法的可行性，现有的数据集的功能磁共振成像测量和消费者的选择。我们发现，与现有的基线方法相比，模型估计的偏差大幅减少（估计系数大致增加一倍），从而改善了推断和样本外需求预测。在控制测量误差后，我们还发现消费者之间模型估计值的变化大大减少。
Recent studies have used the random utility framework to examine whether neural data can assess and predict demand for consumer products, both within and across individuals. However the effectiveness of this methodology has been limited by the large degree of measurement error in neural data. The resulting “error-in-variables” problem severely biases the estimates of the relationship between neural measurements and choice behaviour, thus limiting the role such data can play in assessing marginal contributions to utility. In this article, we propose a method for controlling for this large degree of measurement error in value regions of the brain. We propose that additional neural variables from areas of the brain that are unrelated to valuation can serve as “proxies” for the measurement error in value regions, substantially alleviating the bias in model estimates. We demonstrate the feasibility of our proposed method on an existing dataset of fMRI measurements and consumer choices. We find a substantial reduction in the bias of model estimates compared to existing baseline methods (the estimated coefficients roughly double), leading to improved inference and out-of-sample demand prediction. After controlling for measurement error, we also find a considerable reduction in the variation of model estimates across consumers.