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英文文献：Medium Band Least Squares Estimation of Fractional Cointegration in the Presence of Low-Frequency Contamination-存在低频污染分数协整的中带最小二乘估计
英文文献作者：Bent Jesper Christensen,Rasmus T. Varneskov
英文文献摘要：
This paper introduces a new estimator of the fractional cointegrating vector between stationary long memory processes that is robust to low-frequency contamination such as level shifts, i.e., structural changes in the means of the series, and deterministic trends. In particular, the proposed medium band least squares (MBLS) estimator uses sample dependent trimming of frequencies in the vicinity of the origin to account for such contamination. Consistency and asymptotic normality of the MBLS estimator are established, a feasible inference procedure is proposed, and rigorous tools for assessing the cointegration strength and testing MBLS against the existing narrow band least squares estimator are developed. Finally, the asymptotic framework for the MBLS estimator is used to provide new perspectives on volatility factors in an empirical application to long-span realized variance series for S&P 500 equities.
本文介绍了一种新的平稳长记忆过程的分数协整向量的估计方法，该方法对低频污染，如水平漂移，即序列的结构变化和确定性趋势具有鲁棒性。特别地，提出的中带最小二乘(MBLS)估计器使用样本相关的原点附近的频率微调来解释这种污染。建立了MBLS估计量的相合性和渐近正态性，提出了一种可行的推理方法，并针对已有的窄带最小二乘估计量给出了评估协整强度和检验MBLS估计量的严格工具。最后，MBLS估计量的渐近框架被用来提供新的视角，在经验应用对大跨度已实现的方差序列的标准普尔500股票波动因素。
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