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A GENERALIZED BLOCK BOOTSTRAP FOR SEASONAL TIME SERIES

机译:季节性时间序列的广义块引导

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摘要

When time-series data contain a periodic/seasonal component, the usual block bootstrap procedures are not directly applicable. We propose a modification of the block bootstrap - the generalized seasonal block bootstrap (GSBB) - and show its asymptotic consistency without undue restrictions on the relative size of the period and block size. Notably, it is exactly such restrictions that limit the applicability of other proposals of block bootstrap methods for time series with periodicities. The finite-sample performance of the GSBB is also illustrated by means of a small simulation experiment.
机译:当时间序列数据包含周期性/季节性成分时,通常的块引导程序将不直接适用。我们提议对块引导程序进行修改-广义季节性块引导程序(GSBB)-并显示其渐近一致性,而不会对周期的相对大小和块大小造成不适当的限制。值得注意的是,正是这样的限制限制了块引导程序方法的其他建议对于具有周期性的时间序列的适用性。 GSBB的有限样本性能还通过一个小型模拟实验进行了说明。

著录项

  • 来源
    《Journal of Time Series Analysis》 |2014年第2期|89-114|共26页
  • 作者单位

    Department of Applied Mathematics, AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Krakow, Poland;

    Department of Econometrics, Institute of Mathematics, Cracow University of Technology, Krakow, Poland;

    Department of Mathematics and Statistics, University of Cyprus, PO Box 20537, CY, 1678 Nicosia, Cyprus;

    Department of Mathematics, University of California, San Diego, La Jolla, CA, 92093-0112, USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Periodic time series; resampling; seasonality;

    机译:定期时间序列;重采样季节性;

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