Computationally Efficient Gaussian Maximum Likelihood Methods for Vector ARFIMA Models

94 Pages Posted: 16 Nov 2008

See all articles by Rebecca J. Sela

Rebecca J. Sela

New York University (NYU) - Leonard N. Stern School of Business; J.P. Morgan Chase & Co.

Clifford M. Hurvich

Stern School of Business, New York University; New York University (NYU) - Department of Information, Operations, and Management Sciences

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.

Suggested Citation

Sela, Rebecca J. and Hurvich, Clifford M., Computationally Efficient Gaussian Maximum Likelihood Methods for Vector ARFIMA Models. NYU Working Paper No. SOR-2008-2. Available at SSRN: https://ssrn.com/abstract=1301944

Rebecca J. Sela (Contact Author)

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

J.P. Morgan Chase & Co. ( email )

60 Wall St.
New York, NY 10260
United States

Clifford M. Hurvich

Stern School of Business, New York University ( email )

44 West 4th Street
New York, NY 10012-1126
United States

New York University (NYU) - Department of Information, Operations, and Management Sciences

44 West Fourth Street
New York, NY 10012
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
98
Abstract Views
678
rank
271,071
PlumX Metrics