Semiparametric Bootstrap Methods for Time Series

时间序列的半参数引导方法

• 批准号: 0241152
• 负责人: Bruce Hansen
• 金额: $ 25.13万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0241152&HistoricalAwards=false
• 关键词: Semiparametric; Bootstrap; Methods; Time; Series

ABSTRACTPROPOSAL NUMBER: 0241152INSTITUTION: University of Wisconsin, MadisonNSF PROGRAM: ECONOMICSPRINCIPAL INVESTIGATOR: Hansen, BrucePROPOSAL TITLE: Semiparametric Bootstrap Methods for Time SeriesThe bootstrap is a method of statistical inference is growing in popularity in applied econometrics due to its broad applicability and success in improving the accuracy of statistical inferences. Bootstrap methods for time-series data are fundamentally different from those for random samples, as the bootstrap needs to replicate the dependence structure in the data. The theory for flexible and practical bootstrap methods for time-series is sorely lacking. A promising new bootstrap method for time-series is the Markov bootstrap (MB), which is based on nonparametric estimation of the one-step-ahead conditional distribution function, and uses this estimator to construct the bootstrap distribution. This research extends the MB method to time-series. It investigates theoretical structure of the bootstrap, leading to concrete methods of implementation. The research discusses how to construct the ergodic density of the MB and how to calculate bootstrap parameters and moments from the ergodic density. This is essential for practical implementation of the MB, as these calculations are a necessary input in its application. It also investigates the imposition of constrained on the ergodic density. This is necessary for efficient inference, as a full nonparametric estimator does not make use of the available information about the model. Furthermore, constraints must be imposed when the bootstrap is used to construct efficient confidence intervals.The accuracy of the MB will depend on the accuracy of the nonparametric estimator of the conditional distribution; accordingly the research develops methods to improve estimation efficiency. In this connection, new high order and low-bias kernel estimators explored. The improvements in density estimation lead to improved rates of asymptotic refinements for bootstrap inference. This improvement in density estimation requires theoretical investigation of the methods developed, which this research investigates. The theory and methods developed by this research will have broad impacts on bootstrap applications and will be useful to applied economists in both academic and public sectors.

PROPOSAL编号：0241152 INSTITUTION：威斯康星大学，麦迪逊NSF计划：ECONOMICSPRINCIPAL调查员：Hansen，BrucePROPOSAL标题：时间序列的半参数Bootstrap方法Bootstrap是一种统计推断方法，由于其广泛的适用性和在提高统计推断准确性方面的成功，在应用计量经济学中越来越受欢迎。时间序列数据的Bootstrap方法与随机样本的Bootstrap方法有根本的不同，因为Bootstrap需要复制数据中的依赖结构。关于时间序列的灵活实用的Bootstrap方法的理论严重缺乏。马尔可夫Bootstrap(MB)是一种很有前途的时间序列Bootstrap方法，它基于对先行一步条件分布函数的非参数估计，并使用该估计来构造Bootstrap分布。本研究将MB方法扩展到时间序列。它研究了自举的理论结构，得出了具体的实施方法。讨论了如何构造MB的遍历密度，以及如何由遍历密度计算Bootstrap参数和矩。这对于实际执行甲基溴至关重要，因为这些计算是其应用中的必要投入。并研究了约束对遍历密度的影响。这对于有效的推断是必要的，因为完整的非参数估计器不利用关于模型的可用信息。此外，当使用Bootstrap来构造有效的置信度区间时，必须施加约束，MB的精度将取决于条件分布的非参数估计的精度，因此，研究开发了提高估计效率的方法。在这方面，人们探索了新的高阶和低偏差核估计。密度估计的改进提高了Bootstrap推理的渐近精确率。密度估计的这种改进需要对开发的方法进行理论研究，本研究对此进行了研究。本研究开发的理论和方法将对Bootstrap的应用产生广泛的影响，并将对学术界和公共部门的应用经济学家有用。