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首页> 外文学位 >Two essays in time series analysis: I. Some issues about time series decomposition and seasonal adjustment. II. Asymptotic distributions of some portmanteau statistics for nonstationary time series.
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Two essays in time series analysis: I. Some issues about time series decomposition and seasonal adjustment. II. Asymptotic distributions of some portmanteau statistics for nonstationary time series.

机译：时间序列分析中的两篇文章：I.关于时间序列分解和季节调整的一些问题。二。非平稳时间序列的某些Portmanteau统计量的渐近分布。

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摘要

I. Some issues of time series decomposition and seasonal adjustment are revisited and discussed. The focus is on linking two commonly used decomposition procedures: the Census X-12 procedure and the ARIMA-model-based procedure. First, we provide a model-based interpretation for some options of the X-12 procedure and show that each given filter may correspond to multiple overall models. In practice, we can choose the overall model which fits the data best. However, it is hard to define the whole class of possible overall models and the forms of overall models are very complicated. Second, we provide a guideline on how to select an X-12 filter. It is well-known that the model-based procedure has the identification problem. Each overall model from the data corresponds to multiple component models and each observationally equivalent component model is of equal claim. It motivates us to propose a new criterion based on the average of observationally equivalent component models. In particular, we can select an X-12 filter such that the component estimates from the Census procedure will be closest to the average component models. Empirical results based on an observed series which follows the airline model are presented. Finally, we discuss the problem of forecasting future values of the unobserved components.;II. The goodness-of-fit of a time series model can be tested by some portmanteau statistics. However, existing portmanteau statistics are derived under the stationarity assumption. The results of this paper justify the use of these statistics when the time series is nonstationary. The portmanteau statistic considered here is still asymptotic distributed as chi-squared, but the degrees of freedom have to be adjusted to accommodate the existence of nonstationary characteristic roots. This is because when the time series is nonstationary, the estimates of the characteristic roots on the unit circle converge at a faster rate so that no loss in degrees of freedom is encountered. Simulation studies and a real example show that the consequence of failing to adjust the degrees of freedom can be severe. We also discuss how pretesting the number of the nonstationary roots will affect the portmanteau tests.
机译：一，重新讨论和讨论时间序列分解和季节调整的一些问题。重点在于链接两个常用的分解过程：人口普查X-12过程和基于ARIMA模型的过程。首先，我们为X-12程序的某些选项提供了基于模型的解释，并表明每个给定的过滤器可能对应于多个整体模型。实际上，我们可以选择最适合数据的整体模型。但是，很难定义可能的总体模型的整个类别，并且总体模型的形式非常复杂。其次，我们提供了有关如何选择X-12过滤器的指南。众所周知，基于模型的过程存在识别问题。来自数据的每个总体模型都对应于多个组件模型，并且每个观察上等效的组件模型具有同等的主张。它激励我们基于观察等效组件模型的平均值来提出新的准则。特别是，我们可以选择一个X-12过滤器，以使来自普查程序的组件估算值最接近平均组件模型。提出了基于遵循航空模型的观测序列的经验结果。最后，我们讨论了预测未观察到的成分的未来价值的问题。时间序列模型的拟合优度可以通过某些港口商统计数据进行检验。但是，现有的波特曼酒统计数据是根据平稳性假设得出的。当时间序列不稳定时，本文的结果证明了使用这些统计数据是合理的。这里考虑的portmanteau统计量仍然是渐近分布的卡方，但是必须调整自由度以适应非平稳特征根的存在。这是因为当时间序列不稳定时，单位圆上特征根的估计会以更快的速度收敛，因此不会遇到自由度的损失。仿真研究和一个真实的例子表明，无法调整自由度的后果可能很严重。我们还将讨论非平稳根数的预测试将如何影响portmanteau测试。

著录项

  • 作者

    Chu, Yea-Jane.;

  • 作者单位

    The University of Chicago.;

  • 授予单位 The University of Chicago.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 116 p.
  • 总页数 116
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 宗教;
  • 关键词

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