Computationally Efficient Gaussian Maximum Likelihood Methods for Vector
ARFIMA Models

Rebecca Sela
The Stern School

We will discuss two distinct multivariate time series models that extend
the univariate ARFIMA model. We describe algorithms for computing the
covariances of each model, for computing the quadratic form and
approximating the determinant for maximum likelihood estimation, and for
simulating from each model.  We compare the speed and accuracy of each
algorithm to existing methods and measure the performance of the maximum
likelihood estimator compared to existing methods.  We also fit models to
data on unemployment and inflation in the United States.