Some Issues on Modeling Unobservables, IV

不可观测量建模的一些问题，IV

• 批准号: 9619330
• 负责人: Cheng Hsiao
• 金额: $ 13.67万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-15 至 1999-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9619330&HistoricalAwards=false
• 关键词: Some; Issues; Modeling; Unobservables; IV

Statistical data are often measured with errors. Many of the theoretical constructions of economic, psychological, and sociological models also depend on conceptual variables that are unobservable (e.g., permanent income, rational expectation). To obtain meaningful statistical inference for models containing unobservable variables requires either the expansion of existing data bases with more precise measurements of variables of interest, or the development of analytical frameworks to extract relevant information from currently available data sources. Through the support of previous NSF grants, it has been demonstrated that a structural approach, while often imposing hypothetical assumptions, can yield useful information if properly conducted, and hence not only provides a means to take advantage of information that otherwise might be misused or totally ignored, but is also more economical. This award further investigates the identification, estimation, and statistical inference of (i) nonstationary time series or nonlinear models when variables are subject to random measurement errors; (ii) systematic measurement errors in sample surveys; (iii) the implicit market of attributes which forms the basis of new measurement of inflation rate and other complicated market analysis and; (iv) unobserved heterogeneity in panel data analysis. Classical parametric and nonparametric methods and Bayesian inference via Monte Carlo (Gibbs Sampling) methods will be explored. It is hoped that a successful completion of the project will provide additional repertoire of tools to enhance the reliability and validity of the research usefulness of federal or private statistical data.

统计数据的测量常常有误差。经济学、心理学和社会学模型的许多理论构建也依赖于不可观察的概念变量(例如，永久收入、理性预期)。要对含有不可观测变量的模型进行有意义的统计推断，要么需要扩大现有数据库，更准确地测量感兴趣的变量，要么需要开发分析框架，以便从现有的数据来源中提取相关信息。通过以前国家科学基金会赠款的支持，已经证明，结构方法虽然经常强加假设性假设，但如果处理得当，可以产生有用的信息，因此不仅提供了一种利用否则可能被滥用或完全忽视的信息的手段，而且更经济。该奖项进一步研究了(I)当变量受到随机测量误差时的非平稳时间序列或非线性模型的识别、估计和统计推断；(Ii)抽样调查中的系统性测量误差；(Iii)构成新的通货膨胀率测量和其他复杂市场分析的基础的属性的隐含市场；以及(Iv)面板数据分析中未观察到的异质性。将探索经典的参数方法和非参数方法以及通过蒙特卡罗(Gibbs抽样)方法进行贝叶斯推断。希望该项目的成功完成将提供更多的工具，以提高联邦或私人统计数据的研究有用性的可靠性和有效性。