A Framework for Demand and Pricing Dynamics

需求和定价动态框架

• 批准号: 1130382
• 负责人: Igal Hendel
• 金额: $ 39.31万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1130382&HistoricalAwards=false
• 关键词: Framework; Demand; Pricing; Dynamics

Demand estimation is at the core of economic analysis in many different areas. Examples include cost benefit analysis, antitrust evaluation, and computation of price indices. Considerable progress has been made developing new tool for demand estimation, but most of these tools and their applications have been in static analysis. Demand for most goods, either due to product storability or durability, is dynamic. Namely, intertemporal considerations play an important role, possibly compromising static estimates.Indeed, intertemporal considerations are present in many decisions like schooling, migration, as well as purchasing. In durable good markets falling prices and improved quality provide incentives to delay purchases. In storable goods markets, temporary price reductions create incentives to anticipate future needs. While these dynamic effects are known, and in some cases the bias from ignoring them is well understood, the use of the dynamic models has been limited. Most dynamic methods (specially, those that can be applied to inventory problems) are typically data demanding (require household level data) and computationally intensive. As a result, in academic work as well as in policy applications, dynamics are often neglected. However, neglecting dynamics even when they are not the essence of the question can contaminate the estimates. This award funds the development of new tools to study demand dynamics, as well as optimal seller behavior given such demand. The PIs have a tractable model of consumer demand that can be estimated using market level data. The proposed framework captures dynamic consumer behavior but, unlike the typical inventory model, its predictions are very simple to characterize (and thus, to estimate). The estimation method offered can be done using standard software packages, without the need for time consuming parameter searches. The results of this study have an impact in the field of industrial organization, where demand estimation plays a key role both in academic and policy work. The results also have implications for other fields including macro and trade where recent work has focused on understanding micro-level prices and how they respond to shocks such as exchange rate fluctuations and monetary policy. Both trade and macro work has paid significant attention to pricing in scanner data. One of the key issues faced by all these studies, when using disaggregated high frequency data, is how to deal with temporary price reductions.To show the performance of the method, the model is applied to the soft-drinks industry and used to measure the long-run preference parameters. The proposed demand framework leads to a simple solution to the sellers' pricing problem. Preliminary estimates suggest that the typical pricing patterns of storables, with constant prices and sporadic price reductions, are dictated by intertemproal price discrimination. Intertemporal pricing captures up to 40% of the potential gains from third degree price discrimination between more and less price sensitive buyers. The estimates suggest that both price discrimination and intertemporal price discrimination are welfare enhancing, in a total surplus sense. Less price sensitive consumers are worse off, but more price sensitive ones (shoppers) and sellers gain more than the loss by non-price sensitive consumers.

需求估计是许多不同领域经济分析的核心。这方面的例子包括成本效益分析、反托拉斯评估和价格指数的计算。虽然在开发新的需求估计工具方面取得了很大进展，但这些工具及其应用大多局限于静态分析。对大多数商品的需求，无论是由于产品的可储存性还是耐用性，都是动态的。也就是说，跨期考虑发挥了重要作用，可能会影响静态估计。事实上，跨期考虑存在于许多决策中，如上学，迁移以及购买。在耐用品市场上，价格下跌和质量提高刺激人们推迟购买。在可储存商品市场，临时降价会刺激人们预测未来的需求。虽然这些动态效应是已知的，在某些情况下，忽略它们的偏见也是很好理解的，但动态模型的使用一直受到限制。大多数动态方法（特别是那些可以应用于清单问题的方法）通常需要大量数据（需要家庭一级的数据）和计算密集型。因此，在学术工作和政策应用中，动态往往被忽视。然而，忽略动态，即使它们不是问题的本质，也会污染估计。该奖项资助开发新的工具来研究需求动态，以及在这种需求下的最佳卖家行为。PI有一个易于处理的消费者需求模型，可以使用市场水平数据进行估计。 所提出的框架捕捉动态消费者行为，但与典型的库存模型不同，它的预测非常简单的特点（因此，估计）。所提供的估计方法可以使用标准软件包完成，而不需要耗时的参数搜索。这项研究的结果在产业组织领域产生了影响，需求估计在学术和政策工作中发挥着关键作用。研究结果也对其他领域产生了影响，包括宏观和贸易领域，最近的工作重点是了解微观层面的价格以及它们如何应对汇率波动和货币政策等冲击。贸易和宏观经济工作都非常重视扫描数据中的定价。所有这些研究所面临的关键问题之一，当使用分解的高频数据时，是如何处理暂时的价格reductions.To显示该方法的性能，该模型被应用到软饮料行业，并用于测量长期的偏好参数。建议的需求框架导致一个简单的解决方案，卖方的定价问题。初步估计表明，典型的价格模式，与稳定的价格和零星的价格下降，是由intertemproal价格歧视。跨期定价捕获了对价格敏感的买家和对价格不敏感的买家之间的三度价格歧视的潜在收益的40%。估计表明，无论是价格歧视和跨期价格歧视的福利提高，在总盈余的意义。对价格不太敏感的消费者的情况更糟，但对价格更敏感的消费者（购物者）和卖家的收益大于对价格不敏感的消费者的损失。