Local quadratic spectral and covariance matrix estimation
针对多元时间序列谱密度矩阵在边界频率处的估计问题，提出基于局部多项式回归的新估计量，并应用于通胀与失业数据。
The problem of estimating the spectral density matrix of a multi‐variate time series is revisited with special focus on the frequencies and . Recognizing that the entries of the spectral density matrix at these two boundary points are real‐valued, we propose a new estimator constructed from a local polynomial regression of the real portion of the multi‐variate periodogram. The case is of particular importance, since is associated with the large‐sample covariance matrix of the sample mean; hence, estimating is crucial to conduct any sort of statistical inference on the mean. We explore the properties of the local polynomial estimator through theory and simulations, and discuss an application to inflation and unemployment.