Prewhitening Bias in Hac Estimation
Prewhitening Bias in Hac Estimation
复制标题
Hac 估计中的预白化偏差
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
Chi‐Young Choi
中科院分区：
文献类型：
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作者：
Donggyu Sul;P. Phillips;Chi‐Young Choi
HAC估计通常涉及基于简单自回归模型的预白化过滤器的使用。在这样的应用中，自回归系数估计中的小样本偏差被传递到重新着色滤波器，导致HAC方差估计可能存在严重的偏差。本文利用渐近展开和模拟对这些问题进行了分析。我们推荐的方法包括使用递归降级程序，以减轻小样本自回归偏差的影响。此外，对预白化估计常用的限制规则(一阶自回归系数估计或最大特征值大于0.97的替换为0.97)对单位根检验和KPSS检验的能力产生不利的干扰。我们提出了一种新的边界条件规则，改进了这些测试的大小和功率特性。文中还举例说明了这些调整对KPSS测试的规模和威力的影响。使用预白化的HAC估计和新的边界条件规则，KPSS检验是一致的，与使用传统的预白化HAC估计的KPSS检验相反(Lee，1996)。
HAC estimation commonly involves the use of prewhitening filters based on simple autoregressive models. In such applications, small sample bias in the estimation of autoregressive coefficients is transmitted to the recoloring filter, leading to HAC variance estimates that can be badly biased. The present paper provides an analysis of these issues using asymptotic expansions and simulations. The approach we recommend involves the use of recursive demeaning procedures that mitigate the effects of small sample autoregressive bias. Moreover, a commonly-used restriction rule on the prewhitening estimates (that first order autoregressive coefficient estimates, or largest eigenvalues, greater than 0.97 be replaced by 0.97) adversely interferes with the power of unit root and KPSS tests. We provide a new boundary condition rule that improves the size and power properties of these tests. Some illustrations are given of the effects of these adjustments on the size and power of KPSS testing. Using prewhitened HAC estimates and the new boundary condition rule, the KPSS test is consistent, in contrast to KPSS testing that uses conventional prewhitened HAC estimates (Lee, 1996).