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Testing the null of stationarity for multiple time series

机译:测试多个时间序列的平稳性为空

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This paper introduces Various consistent tests for the null of stationrity against the alternative of nonstationarity applicable to multiple time series with and without the presence of time trends. The tests are based on the multivariate AR(I) model and derived by the principles of AR unit root tests. An important feature of the tests from the practical viewpoint is that no a priori knowledge about the data generating process of the series under study is required when the lag length for the long-run variance estimation is estimated by using Andrews (1991 Econometrica 59, 812-858) automatic lag selection methods along with a simple inequality restriction. This simple restriction also make the tests diverge at faster rates. The asymptotic distributions of these tests are complex and nonstandard but expressed in a unified manner by using the standard vector Brownian motion. The distributions are tabulated by simulation for some practical cases. The rates of divergence under the alternative are also reported. Further, the asymptotic effects of misspecifying the order of time trends in the regression model are analyzed. Using the regression model which does not detrend time series properly results in rejecting the null hypothesis in large samples even when the null is true. Extensive simulation illustrates the finite same performance of the tests introduced in this paper. The multivariate tests using Andrews' automatic lag selection methods with a restriction work reasonably well in finite samples. In particular, it is illustrated that using the multivariate tests introduced in this paper is a better testing strategy for detrended time series in terms of the finite sample size and power than applying univariate tests several times to each component of a multiple time series. The tests are applied to the real interest rates of the major industrialized nations studied in Kugler and Neusser (1993, Journal of Applied Econometrics 8, 163-174). The null of Stationarity is not rejected for the real interest rates at conventional significance levels.
机译:本文介绍了针对平稳性为零的各种一致性测试,以及适用于多个时间序列(无论是否存在时间趋势)的非平稳性选择。这些测试基于多元AR(I)模型,并通过AR单位根测试的原理得出。从实践的角度来看,检验的一个重要特征是,当使用Andrews(1991 Econometrica 59,812)估算长期方差估计的滞后长度时,不需要先验知识即可了解所研究系列的数据生成过程。 -858)自动滞后选择方法以及简单的不等式限制。这种简单的限制也使测试以更快的速度发散。这些检验的渐近分布是复杂且非标准的,但是通过使用标准矢量布朗运动以统一的方式表示。通过仿真将某些实际情况的分布表化。还报告了替代方案下的差异率。此外,分析了在回归模型中错误指定时间趋势顺序的渐近效果。使用不能正确地使时间序列偏离趋势的回归模型会导致即使大样本为真,也拒绝大样本中的假假设。广泛的仿真说明了本文介绍的测试的有限相同性能。使用安德鲁斯的自动滞后选择方法进行的多变量测试在有限样本中可以很好地工作。尤其是,可以证明,与在多个时间序列的每个分量上多次应用单变量测试相比,使用有限变量的样本大小和功效,使用本文介绍的多元测试是一种更好的策略。该测试适用于在Kugler和Neusser中研究的主要工业化国家的实际利率(1993年,Journal of Applied Econometrics 8,163-174)。对于常规显着性水平下的实际利率,不会拒绝Stationarity的空值。

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