Abstract #300582

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JSM 2003 Abstract #300582
Activity Number: 75
Type: Topic Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Business & Economics Statistics Section
Abstract - #300582
Title: A Non-Gaussian Airline Model for Seasonal Adjustment
Author(s): John Aston*+ and Siem Jan Koopman
Companies: NISS/U.S. Census Bureau and Free University
Address: Statistical Research Division, Washington, DC, 20233-9100,
Keywords: Seasonal Adjustment ; Airline Model ; ARIMA models ; Heavy Tailed Distributions ; State Space Modelling
Abstract:

The Airline model, introduced by Box and Jenkins in their seminal book Time Series Analysis:Forecasting and Control, is routinely used to model economic time series. The model is parameterized by two factors, and gaussianity is usually assumed for the underlying noise component. Here, this model is generalised to include a non-gaussian component to model outliers in the data. The model is examined using a state-space modelling approach, and importance sampling (see Durbin and Koopman). It utilises the decomposition method for ARIMA models developed by Hillmer and Tiao. This is necessary in order to preserve the airline structure whilst allowing a flexibility to include non-gaussian noise terms for different components in the model. Different forms for the generalisation of the noise term are investigated, so as to determine the optimal number of parameters which allow estimates not only to be well determined but also to allow adequate modelling of the data. The models are interrogated through the use of both simulated and real series. The new models allow outliers to be accounted for, whilst keeping the underlying structures that are currently used to aid reporting of economic data

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