JSM 2004 - Toronto

Abstract #300924

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Activity Number: 161
Type: Contributed
Date/Time: Monday, August 9, 2004 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #300924
Title: Temporal Aggregation of Stationary and Nonstationary Discrete-time Processes
Author(s): Henghsiu Tsai*+ and Kung-sik Chan
Companies: Institute of Statistical Science, Academia Sinica and University of Iowa
Address: 128, Academia Rd. Sec. 2, Taipei , 115, Taiwan
Keywords: asymptotic efficiency of prediction ; autocorrelation ; ARFIMA models ; long memory ; spectral density
Abstract:

We study the autocorrelation structure and the spectral density function of aggregates from a discrete-time process. The underlying discrete-time process is assumed to be a stationary AutoRegressive Fractionally Integrated Moving-Average (ARFIMA) process, after suitable number of differencing if necessary. We derive closed-form expressions for the limiting autocorrelation function and the normalized spectral density of the aggregates, as the extent of aggregation increases to infinity. These results are then used to assess the loss of forecasting efficiency due to aggregation.

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