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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27727
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| Title: | Computationally Efficient Gaussian Maximum Likelihood Methods for Vector
ARFIMA Models |
| Authors: | Sela, Rebecca J.
Hurvich, Clifford M. |
| Issue Date: | 13-Oct-2008 |
| Series/Report no.: | SOR-2008-2 |
| Abstract: | In this paper, we 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, to data on goods and services inflation in the United
States, and to data about precipitation in the Great Lakes. |
| URI: | http://hdl.handle.net/2451/27727 |
| Appears in Collections: | IOMS: Statistics Working Papers
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