A multivariate preconditioned conjugate gradient approach for maximum likelihood estimation in vector long memory processes

Abstract

We present an approach via a multivariate preconditioned conjugate gradient (MPCG) algorithm for maximum likelihood estimation of parameters from vector ARFIMA models with Gaussian errors. This approach involves the solution of a block-Toeplitz system, and Monte Carlo integration over the process history.