Semiparametric Bootstrap Methods for Time Series

摘要提案编号：0241152机构： 威斯康星州，麦迪逊NSF大学： 经济型基本制冷剂：汉森，布鲁斯提案标题：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成功地提高统计推断的准确性，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。 灵活实用的时间序列引导方法的理论非常缺乏。马尔可夫自举（Markov bootstrap，MB）是一种基于一步前条件分布函数的非参数估计，并利用该估计量构造自举分布的方法。本研究将MB方法推广到时间序列。 它调查的理论结构的引导，导致具体的实施方法。 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的必要投入。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量不使用关于模型的可用信息。 此外，利用Bootstrap方法构造有效的置信区间时，必须施加一定的约束条件，MB的精度将取决于条件分布的非参数估计的精度，因此本研究提出了提高估计效率的方法。 在这方面，新的高阶和低偏差的核估计进行了探索。 密度估计的改进提高了自举推理的渐近精化率。 这种改进的密度估计需要理论研究的方法，本研究调查。本研究所发展的理论和方法将对自助法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。

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.