Identification and Estimation of Industry Dynamic Models with Persistent and Hidden State Variables

具有持久和隐藏状态变量的行业动态模型的识别和估计

• 批准号: 0137048
• 负责人: Jeffrey Campbell
• 金额: $ 11.82万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-01 至 2007-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0137048&HistoricalAwards=false
• 关键词: Identification; Estimation; Industry; Dynamic; Models

This research project focuses on the formulation and estimation of structural models of producer entry, growth, and exit. The data source is new; a file of alcohol tax returns that contains the date of birth, date of exit, and a complete monthly history of the dollar value of alcohol sales for all licensed restaurants and bars in Texas. In the model, a firm's sales is proportional to its profits, but transitory cost shocks make it an imperfect indicator of profitability's persistent component. Because of this imperfect observation, the model's state variable is both persistent and hidden. The state variable's persistence distinguishes this model from the many estimable models of dynamic discrete choice that incorporate Rust's (1987) conditional independence assumption. To disentangle the persistent and transitory components of profitability, this project uses the fact that producers' exit decisions depend only on the persistent component. In the model with normally distributed shocks, the information in producers' exit decisions identifies the parameters describing both the persistent and transitory components of profitability as well as the producer's optimal exit threshold. An important component of this research project is the extension of this identification proof to semi-parametric and non parametric environments. The project's initial empirical research focuses on distinguishing Gaussian models of entrepreneurial learning similar to Jovanovic's (1982) from models with perfect entrepreneurial information, such as Hopenhayn's (1992). Additional information in the data set describing each restaurant or bar's location and the characteristics of its parent firm suggest further generalizations of the model and estimation technique.

该研究项目着重于生产者进入，增长和退出的结构模型的制定和估计。 数据源是新的；包括出生日期，退出日期以及德克萨斯州所有许可餐厅和酒吧的酒精销售价值的完整每月历史的酒精纳税申报表。在模型中，公司的销售额与其利润成正比，但是暂时性的成本冲击使其成为盈利能力持久组件的不完美指示。由于这种不完善的观察，模型的状态变量既持久又隐藏。状态变量的持久性将该模型与许多可估计的动态离散选择模型区分开来，该模型结合了Rust（1987）的条件独立性假设。为了删除盈利能力的持久和暂时性组成部分，该项目使用了生产商的退出决策仅取决于持久组件的事实。在具有正态分布冲击的模型中，生产者退出决策中的信息确定了描述获利能力的持久性和暂时性成分以及生产者的最佳出口阈值的参数。该研究项目的一个重要组成部分是将此识别证明扩展到半参数和非参数环境。该项目的最初实证研究着重于区分与Jovanovic（1982）的创业学习模型，从具有完美的企业家信息的模型（例如Hopenhayn's（1992））。描述每个餐厅或酒吧的位置以及其母公司的特征的数据集中的其他信息表明了模型和估计技术的进一步概括。