ROBUST COVARIANCE MATRIX ESTIMATION: HAC ESTIMATES WITH LONG MEMORY/ANTIPERSISTENCE CORRECTION
针对向量过程元素存在长记忆或反持久性时传统谱密度估计不一致的问题，提出一种自动适应未知记忆参数的稳健估计方法。
Smoothed nonparametric estimates of the spectral density matrix at zero frequency have been widely used in econometric inference, because they can consistently estimate the covariance matrix of a partial sum of a possibly dependent vector process. When elements of the vector process exhibit long memory or antipersistence such estimates are inconsistent. We propose estimates which are still consistent in such circumstances, adapting automatically to memory parameters that can vary across the vector and be unknown.