Size and Power of Tests for Stationarity in Highly Autocorrelated Time Series
Size and Power of Tests for Stationarity in Highly Autocorrelated Time Series
复制标题
高度自相关时间序列平稳性检验的规模和功效
DOI：
10.2139/ssrn.371980
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
Ulrich K. Müller
中科院分区：
文献类型：
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作者：
Ulrich K. Müller
平稳性测试通常应用于高度持久的时间序列。在Kwiatkowski、菲利普斯、施密特和Shin（1992）之后，标准平稳性通过（潜在）平稳序列的长期方差的估计量进行重新标度。本文分析研究的规模和权力的性质，这样的测试时，一系列的强自相关的局部到统一的渐近框架。结果表明，测试的行为强烈依赖于长期方差估计，但在一般情况下是非常不可取的。无论是测试无法控制的大小，即使是强烈的意思回复系列，或者他们是不一致的综合过程和歧视只有很差的固定和综合过程相比，最佳统计。
Tests for stationarity are routinely applied to highly persistent time series. Following Kwiatkowski, Phillips, Schmidt and Shin (1992), standard stationarity employs a rescaling by an estimator of the long-run variance of the (potentially) stationary series. This paper analytically investigates the size and power properties of such tests when the series are strongly autocorrelated in a local-to-unity asymptotic framework. It is shown that the behavior of the tests strongly depends on the long-run variance estimator employed, but is in general highly undesirable. Either the tests fail to control for size even for strongly mean reverting series, or they are inconsistent against an integrated process and discriminate only poorly between stationary and integrated processes compared to optimal statistics.