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AI 根据所提供的论文材料归类:摘要研究高频平稳与协整回归的时间序列稳健推断,未明确金融资产背景。

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关注这篇论文的作者
Taeyoon Hwang

长时间跨度高频回归的若干 fixed-b 结果

研究问题
在长时间跨度的高频平稳及协整回归中,异方差与自相关稳健 Wald 检验的 fixed-b 极限及临界值应如何确定?
方法与思路
作者在连续时间框架下推导两类回归的 fixed-b 极限,并用模拟比较不同数据依赖带宽和临界值的拒绝表现。
材料陈述的贡献
摘要称,平稳回归具有标准 fixed-b 极限,协整情形可能非枢轴;模拟中 fixed-b 临界值使拒绝率更接近名义水平。
阅读关注点
应核对高频与长跨度的联合渐近框架、带宽的高频兼容性,以及协整极限何时枢轴化和调参如何影响检验。
阅读边界
仅据 RePEc 出版社摘要;渐近条件与模拟数值未说明,临界值改善限于所述设置,摘要也未明确数据的金融应用。

原文摘要

This paper develops fixed-b asymptotic results for heteroskedasticity autocorrelation robust (HAR) Wald tests for high frequency data using the continuous time framework of Chang et al. (2023) (CLP). It is shown that the fixed-b limit of HAR Wald tests for high frequency stationary regressions is the same as the standard fixed-b limit in Kiefer and Vogelsang (2005). For the case of cointegrating regression the form of the fixed-b limits are different from the stationary case and may or may not be pivotal but also have the same fixed-b limits that have been obtained for tests based on ordinary least squares (OLS) (Bunzel, 2006) and integrated modified OLS (Vogelsang and Wagner, 2014). A simulation study shows that fixed-b critical values provide rejection probabilities closer to nominal levels than traditional chi-square critical values when using data-dependent bandwidths. The Andrews (1991) data-dependent method works reasonably well for a wider range of persistence parameters than those considered by CLP. In contrast, the Newey and West (1994) data-dependent method is sensitive to the choice of pre-tuning parameters. The data-dependent method of Sun et al. (2008) give results similar to the Andrews (1991) method with slightly less over-rejection problems when used with fixed-b critical values. Our results for bandwidth choice reinforce the importance of high frequency compatibility of bandwidths as emphasized by CLP. Regardless of the bandwidth method used in practice, it is clear that fixed-b critical values can and should be used for high frequency data whenever HAR tests are based on kernel estimators of long run variances. Our results complement the analysis of Pellatt and Sun (2023) who focused on HAR tests based on orthonormal series estimators of long run variance estimator.

笔记与分组
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