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Activity Number: 570
Type: Contributed
Date/Time: Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract - #304740
Title: Temporal Aggregation of Long Memory Processes and ARFIMA Approximations
Author(s): Ka S. Man*+
Companies: Western Illinois University
Address: Information Systems and Decision Sciences Dept, Macomb, IL, 61455,
Keywords: Long memory process ; Temporal aggregation ; Time Series
Abstract:

The limiting aggregate structure of long memory processes is derived in Tsai and Chan (2005a, b), and Tsai (2006) remarks that the limiting structure can be regarded as an extension of fractional Gaussian noise. Man and Tiao (2006, 2009) propose and study a class of low-order ARFIMA (0, d, d*) models in approximating the exact limiting structure. In this paper, we study a class of ARFIMA (0, d, q) models with very stable autocorrelation structures upon temporal aggregation. We find that such class of ARFIMA (0, d, q) models better approximates the limiting autocorrelations when the d value is small, and the two classes of approximations are essentially the same as the d value gets larger. The forecast efficiency in predicting future aggregates is also discussed.

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