Nonparametric Likelihood Methods for Dynamic Econometric Models: Theory and Application

动态计量经济模型的非参数似然法：理论与应用

• 批准号: 9632101
• 负责人: Yuichi Kitamura
• 金额: $ 8.7万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-01 至 1999-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9632101&HistoricalAwards=false
• 关键词: Nonparametric; Likelihood; Methods; Dynamic; Econometric

Yuichi Kitamura University of Minnesota - Twin Cities SBR-9632101 This research will investigate parametric likelihood methods. Parametric likelihood methods are central to econometric and statistical research. In general, parametric methods involve the use of rather arbitrary assumptions on the data generating process. This research will build on recent literature on nonparametric likelihood methods which extends the concept of likelihood to models without distributional assumptions. One of the contributions of this research is to extend various empirical likelihood methods to time series observations by treating the dependence structure nonparametrically. Extensive simulation studies will be undertaken to examine the nature of these new methods. In addition, the project will investigate a new criterion for model comparison, looking at various specifications of dynamic asset pricing models.

北村雄一明尼苏达大学双城分校SBR-9632101 本研究将探讨参数似然方法。 参数似然方法是计量经济学和统计学研究的核心。 一般来说，参数方法涉及在数据生成过程中使用相当任意的假设。 这项研究将建立在最近的文献上的非参数似然方法，扩展的概念，没有分布的假设模型的可能性。 本研究的贡献之一是通过非参数化处理相关结构，将各种经验似然方法扩展到时间序列观测。 将进行广泛的模拟研究来检查这些新方法的性质。 此外，该项目将研究模型比较的新标准，研究动态资产定价模型的各种规格。