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Testing economic time series for stationarity and nonstationarity.

机译:测试经济时间序列的平稳性和非平稳性。

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摘要

It is a well-established empirical fact that standard unit root tests fail to reject the unit root hypothesis for many economic time series. However, these results do not indicate strong evidence against relevant trend stationarity alternatives, because it is well-known that unit root tests are not very powerful.;This dissertation extends the KPSS test statistic for stationarity in two ways. First, finite sample size and power of the KPSS statistic for stationarity are extensively studied in a Monte Carlo experiment. Next the use of the KPSS statistic as a unit root test is suggested, because the KPSS statistic is consistent and a different limning distribution is obtained under the hypothesis that the series is difference stationary.;Both tests are applied to the Nelson-Plosser data, and for many of these series it is not very clear whether they contain a unit root or are trend stationary. These results are quite consistent with recent (inconclusive) empirical findings.;One implication of the above empirical findings is that many economic time series may be in the region of "near stationarity." A lot of Monte Carlo studies have shown that standard unit root tests have severe size distortions when the process is nearly stationary. This dissertation also considers the asymptotics of standard unit root tests in this case using generalized "nearly stationary model." It is found that the above size distortion problem is well predicted by our asymptotics. It is also argued that the superiority of the augmented Dickey-Fuller statistic is not established and that more efficient estimation techniques will be needed to improve the tradeoff between size distortions and low power.;Recently, various attempts, including a Bayesian approach, have been made to reconsider the important problem of distinguishing trend stationary and unit root processes. However, there have been very few previous attempts to test the null hypothesis of stationarity directly. Kwiatkowski, Phillips, Schmidt, and Shin (1992, KPSS) propose an LM test of the null hypothesis that an observable series is stationary around a deterministic trend, using the components representation in which the series is decomposed into the sum of deterministic trend, random walk, and stationary error.
机译:一个公认的经验事实是,在许多经济时间序列中,标准单位根检验不能拒绝单位根假设。但是,这些结果并不能提供强有力的证据来证明相关趋势平稳性的替代方案,因为众所周知,单位根检验的功能不是很强大。本论文从两个方面扩展了平稳性的KPSS检验统计量。首先,在蒙特卡洛实验中,对平稳性的KPSS统计量的有限样本量和功效进行了广泛研究。接下来,建议使用KPSS统计量作为单位根检验,因为KPSS统计量是一致的,并且在该序列是差分平稳的假设下获得了不同的间隔分布。;两种检验都应用于Nelson-Plosser数据,对于其中的许多序列,尚不清楚它们是否包含单位根或趋势是否稳定。这些结果与最近的(不确定的)经验结果是一致的。上述经验结果的一个含义是,许多经济时间序列可能处于“接近平稳”区域。大量的蒙特卡洛研究表明,当过程接近平稳时,标准的单位根测试具有严重的尺寸失真。本文还考虑了在这种情况下使用广义“近乎平稳模型”的标准单位根检验的渐近性。发现上述尺寸失真问题已由我们的渐近性很好地预测。也有人认为增强Dickey-Fuller统计量的优越性尚未确立,需要更有效的估算技术来改善尺寸失真和低功耗之间的折衷。最近,人们进行了各种尝试,包括贝叶斯方法。以重新考虑区分趋势平稳过程和单位根过程的重要问题。但是,以前很少有尝试直接检验平稳性的零假设的尝试。 Kwiatkowski,Phillips,Schmidt和Shin(1992,KPSS)提出了一个零假设的LM检验,该假设是一个可观测的序列在确定性趋势附近平稳,它使用分量表示将序列分解为确定性趋势的总和,随机行走和静止错误。

著录项

  • 作者

    Shin, Yongcheol.;

  • 作者单位

    Michigan State University.;

  • 授予单位 Michigan State University.;
  • 学科 Economic theory.
  • 学位 Ph.D.
  • 年度 1992
  • 页码 184 p.
  • 总页数 184
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:50:19

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