Statistical Inference of Models with Time-Varying Parameters

时变参数模型的统计推断

• 批准号: 0906073
• 负责人: Wei Biao Wu
• 金额: $ 24.43万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0906073&HistoricalAwards=false
• 关键词: Statistical; Inference; Models; Time; Varying

The investigator studies estimation, testing and construction of simultaneous confidence regions for time-varying parameters in models with non-stationary and dependent errors. The simultaneous confidence regions can be used to test patterns of associations between covariates and responses. The investigator also develops new tools for asymptotic analysis of non-stationary time series.During the last decade, models with time-varying data generating mechanisms have gained substantial attention in various fields including economics, finance, engineering, sociology, medical science, environmental science among others. Results from the proposal are useful for understanding the dynamic association between explanatory variables and responses for temporally observed data in which the underlying physical mechanism change with respect to time.

研究人员研究具有非平稳和相关误差的模型中时变参数的同时置信区域的估计、测试和构建。同时置信区域可用于测试协变量和响应之间的关联模式。研究人员还开发了用于非平稳时间序列渐近分析的新工具。在过去的十年中，具有时变数据生成机制的模型在经济、金融、工程、社会学、医学、环境科学等各个领域获得了广泛关注除其他外。该提案的结果有助于理解解释变量与时间观测数据的响应之间的动态关联，其中底层物理机制随时间变化。