JSM 2004 - Toronto

Abstract #300548

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Activity Number: 214
Type: Topic Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Business and Economics Statistics Section
Abstract - #300548
Title: Deterministic and Stochastic Linear and Nonlinear Mean Prediction of Time Series
Author(s): Simone Giannerini*+
Companies: University of Bologna
Address: Via delle belle arti 41, Bologna, International, 40126, Italy
Keywords: time series ; detrend ; prediction ; inference ; deterministic trend ; stochastic trend
Abstract:

Two common approaches are usually followed to render mean stationary an observed time series. The first one consists in removing a deterministic polynomial trend, so that the adjusted series is said to be trend stationary. The second approach makes use of the regular difference operator of a low order; in this instance, the detrended series is stochastic stationary. We compare the results of applying both the methods to a plethora of series whose trend component can be either deterministic or stochastic. The effect of every choice on the subsequent analysis to be performed on the series will be investigated. In particular, we will assess to what extent the use of the wrong detrender affects the true generating structure of the series under question.

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