Empirical Models of Inventory Investment and Price Determination by Durable Commodity Intermediaries

库存投资和耐用商品中介定价的实证模型

• 批准号: 9905145
• 负责人: George Hall
• 金额: $ 22.69万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-01 至 2003-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9905145&HistoricalAwards=false
• 关键词: Empirical; Models; Inventory; Investment; Price

This research develops and empirically tests dynamic programming models of inventory investment and price determination by durable commodity intermediaries. These firms acquire bulk quantities of durable commodities such as steel, lumber, or coal from producers and subsequently sell their inventories to customers at a markup. The investigators have established a working relationship with one such intermediary, a U.S. steel supplier, which has provided them with detailed daily data on purchases, sales, and inventory holdings for over 2,000 products since June, 1997 with monthly updates on an on-going basis. A major component of the research is micro-oriented, focusing on developing computational and econometric methods to determine whether the firm's behavior can be accurately approximated as an optimal trading strategy from the solution to a dynamic programming problem. Since the data provide identities of each of the intermediary's customers and the prices and quantities they purchased, the investigators can study whether dynamic models of optimal price discrimination can explain significant variations in prices charged to different customers. The project develops new econometric procedures for correcting the problem of dynamic selectivity bias resulting from endogenous sampling of the prices at which the intermediary acquires new inventories. The research also draws macro-level implications by aggregating the micro-level models to develop a market-level (disequilibrium) model of price determination. The investigators analyze the impact of macro shocks and cyclical fluctuations in interest rates on the behavior of these markets to provide new insights into the role of inventories as propagating or stabilizing mechanisms in business cycles. Finally, this research program extends the state of the art on numerical methods for continuous dynamic programming problems such as the inventory problem by comparing discrete approximation methods such as uniform discretization, quadrature-based methods, and random and low-discrepancy multi-grid methods to continuous approximation methods based on polynomial and neural network approximations to the value function and decision rule. The investigators also evaluate Euler equation methods (e.g. parameterized expectations), and methods from the AI/learning literature such as "Q-learning" and neuro-dynamic programming.

本研究开发并实证测试了耐用商品中介的库存投资和价格决定的动态规划模型。这些公司从生产商处购买大量耐用商品，例如钢铁、木材或煤炭，然后以加价将库存出售给客户。调查人员与这样的中间机构（一家美国钢铁供应商）建立了工作关系，该供应商为他们提供了自 1997 年 6 月以来 2,000 多种产品的详细每日采购、销售和库存数据，并每月持续更新。 该研究的一个主要组成部分是微观导向的，重点是开发计算和计量经济学方法，以确定公司的行为是否可以从动态规划问题的解决方案中准确地近似为最佳交易策略。由于数据提供了每个中介客户的身份以及他们购买的价格和数量，因此研究人员可以研究最优价格歧视的动态模型是否可以解释向不同客户收取的价格的显着差异。该项目开发了新的计量经济学程序，以纠正由于对中介机构获取新库存的价格进行内生抽样而产生的动态选择性偏差问题。 该研究还通过汇总微观层面的模型来开发市场层面（不均衡）的价格决定模型，从而得出宏观层面的影响。研究人员分析了宏观冲击和利率周期性波动对这些市场行为的影响，为库存作为商业周期传播或稳定机制的作用提供新的见解。 最后，该研究项目通过将离散逼近方法（例如均匀离散化、基于正交的方法以及随机和低差异多重网格方法）与基于多项式和神经网络逼近值函数和决策规则的连续逼近方法进行比较，扩展了连续动态规划问题（例如库存问题）数值方法的最新技术。研究人员还评估了欧拉方程方法（例如参数化期望）以及人工智能/学习文献中的方法，例如“Q-学习”和神经动态编程。