Estimation of Long Memory Linear Processes

[摘要] This paper studies asymptotic properties of the minimum distance Hellinger estimates for a stationary ultivariate linear gaussien long range dependent process of the form , where is a sequence of strictly stationary d-dimensional associated random vectors with E(Zt)= 0 and and {Au} is a sequence of coefficient matrices with and . By means of the properties of the kernel density estimate, the minimum istance Hellinger of this class are shown to be consistent, asymptotically normal and robust.

[关键词] Multivariate Processes;Kernel Density;Hellinger Distance;Linear Process;Parametric Estimation;Long Memory;Multivariate Processes