Multistep Forecasting of Long Memory Series Using Fractional Exponential Models

13 Pages Posted: 31 Oct 2008

See all articles by Clifford Hurvich

Clifford Hurvich

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

Date Written: 2000

Abstract

We develop forecasting methodology for the fractional exponential (FEXP) model. First, we devise algorithms for fastexact computation of the coefficients in the infinite order autoregressive and moving average representations of a FEXPprocess. We also describe an algorithm to accurately approximate the autocovariances and to simulate realizations of theprocess. Next, we present a fast frequency-domain cross validation method for selecting the order of the model. This modelselection method is designed to yield the model which provides the best multistep forecast for the given lead time, withoutassuming that the process actually obeys a FEXP model. Finally, we use the infinite order autoregressive coefficients of afitted FEXP model to construct multistep forecasts of inflation in the United Kingdom. These forecasts are substantiallydifferent than those from a fitted ARFIMA model.

Keywords: Fractional integration, Long-range dependence, Spectral factorization

Suggested Citation

Hurvich, Clifford, Multistep Forecasting of Long Memory Series Using Fractional Exponential Models (2000). NYU Working Paper No. SOR-2000-10, Available at SSRN: https://ssrn.com/abstract=1290972

Clifford Hurvich (Contact Author)

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

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

44 West Fourth Street
New York, NY 10012
United States

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