论文标题
对具有固定效应的分化产品的动态需求未观察到异质性
Dynamic demand for differentiated products with fixed-effects unobserved heterogeneity
论文作者
论文摘要
本文研究了使用消费者级的面板数据对差异化产品的动态离散选择模型的识别和估计，而每个消费者的购买活动很少（即短面板）。消费者是前瞻性的，他们的偏好结合了两种动态来源：由于习惯和转换成本以及由于库存，折旧或学习而引起的持续时间依赖性引起的最后选择依赖性。该模型的一个关键区别特征是，消费者未观察到的异质性具有固定效应（FE）结构 - 也就是说，其概率分布在内源性状态变量的初始值的条件不受限制。我应用并扩展了最新结果，以确定所有结构参数，只要数据集包括每个家庭四次或更多的购买活动。可以使用足够的统计 - 条件最大似然（CML）方法来估计参数。 CML在此模型中的一个有吸引力的功能是，对于消费者决策问题的前瞻性值的足够统计控制，因此该方法不需要解决动态编程问题或计算预期的当前值。
This paper studies identification and estimation of a dynamic discrete choice model of demand for differentiated product using consumer-level panel data with few purchase events per consumer (i.e., short panel). Consumers are forward-looking and their preferences incorporate two sources of dynamics: last choice dependence due to habits and switching costs, and duration dependence due to inventory, depreciation, or learning. A key distinguishing feature of the model is that consumer unobserved heterogeneity has a Fixed Effects (FE) structure -- that is, its probability distribution conditional on the initial values of endogenous state variables is unrestricted. I apply and extend recent results to establish the identification of all the structural parameters as long as the dataset includes four or more purchase events per household. The parameters can be estimated using a sufficient statistic - conditional maximum likelihood (CML) method. An attractive feature of CML in this model is that the sufficient statistic controls for the forward-looking value of the consumer's decision problem such that the method does not require solving dynamic programming problems or calculating expected present values.
