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可能部分非平稳向量时间序列的缺失值估计AI 解读

Ljung's (1989) method for estimating missing values and evaluating the corresponding likelihood function in scalar time series is extended to the vector case. The series is assumed to be generated by a possibly partially nonstationary and noninvertible vector autoregressive-moving average process. No particular pattern of missing data is assumed. Future and past values are special cases of missing data that can be estimated in the same way. The method does not use Kalman…

长序列向量一般线性过程的快速似然近似：分数差分应用AI 解读

The quadratic form appearing in the likelihood function of a Gaussian stationary and invertible vector general linear process is considered in this paper. A new expression for this quadratic form is developed, which allows repeated computations to be performed very efficiently by possibly using forecast and backcast values of the series. While the quadratic form itself involves the inverse of the covariance matrix, the new expression uses the autocovariances of the…