Nonparametric Likelihood Methods for Dynamic Econometric Models: Theory and Application

北村雄一明尼苏达大学双子城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。一般而言，参数方法涉及在数据生成过程中使用相当随意的假设。这项研究将建立在最近关于非参数似然方法的文献的基础上，非参数似然方法将似然的概念扩展到没有分布假设的模型。本研究的贡献之一是通过非参数处理相依结构，将各种经验似然方法推广到时间序列观测中。将进行广泛的模拟研究，以检验这些新方法的性质。此外，该项目将研究一种新的模型比较标准，考察各种规格的动态资产定价模型。

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.