Modelling Unobservable Variables

不可观测变量建模

• 批准号: 9122481
• 负责人: Cheng Hsiao
• 金额: $ 11.33万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-01 至 1994-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9122481&HistoricalAwards=false
• 关键词: Modelling; Unobservable; Variables

Models containing unobservable variables arise very often in economics, psychology, and other social sciences. They may arise because of measurement errors, or because behavioral responses are in part determined by unobservable characteristics of agents. To obtain meaningful statistical inferences for models containing unobservable variables requires either the expansion of existing data bases together with more precise measurements of variables of interest or the development of analytical frameworks to extract information from currently available data sources. The aim of this project is to continue developing analytical frameworks to enhance the reliability and validity of Federal statistical data. Attention is directed to cases where the use of latent variable models by researchers appears appropriate because of measurement errors, or because of the lack of measurable counterparts for the conceptual variables. The investigation centers on three issues (i) categorical or grouped data, (ii) the hedonic approach to demand analysis, and (iii) modelling unobserved heterogeneity in panel data analysis. The overall objective is to refine the methodology to establish empirically valid inferences. Professor Hsiao's research on modelling unobservable variables has been very fruitful. While his analytical approach, often requires the imposition of hypothetical assumptions, he has shown that it can yield useful information when properly conducted. Not only does the approach provide a means to take advantage of information that might otherwise be misused or totally ignored, it also can accomplish this desirable goal at relatively low cost.

包含不可观察变量的模型经常出现在经济学、心理学和其他社会科学中。它们的出现可能是因为测量误差，或者因为行为反应在一定程度上是由主体不可观察的特征决定的。要对包含不可观察变量的模型获得有意义的统计推断，要么需要扩大现有数据库，同时对感兴趣的变量进行更精确的测量，要么需要开发分析框架，以便从当前可用的数据源中提取信息。该项目的目的是继续发展分析框架，以提高联邦统计数据的可靠性和有效性。由于测量误差，或由于缺乏概念变量的可测量对应物，研究人员使用潜在变量模型似乎是合适的，因此应注意这些情况。调查集中在三个问题上：(i)分类或分组数据，（ii）需求分析的享乐方法，以及（iii）面板数据分析中未观察到的异质性建模。总体目标是完善方法，以建立经验有效的推论。肖教授对不可观测变量建模的研究成果非常丰富。虽然他的分析方法经常需要强加假设，但他已经证明，如果进行得当，它可以产生有用的信息。该方法不仅提供了一种利用可能被滥用或完全忽略的信息的方法，而且还可以以相对较低的成本实现这一理想的目标。