Linear signal extraction with intervention techniques in non-linear time series

✍ Scribed by Christophe Planas

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 134 KB
• Volume: 17
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/(sici)1099-131x(199812)17:7<515::aid-for678>3.0.co;2-v

✦ Synopsis

Seasonal adjustment is performed in some data-producing agencies according to the ARIMA-model-based signal extraction theory. A stochastic linear process parametrized in terms of an ARIMA model is ®rst ®tted to the series, and from this model the models for the trend, cycle, seasonal, and irregular component can be derived. A spectrum is associated to every component model and is used to compute the optimal Wiener± Kolmogorov ®lter. Since the modelling is linear, prior linearization of the series with intervention techniques is performed. This paper discusses the performance of linear signal extraction with intervention techniques in nonlinear processes. In particular, the following issues are discussed: (1) the ability of intervention techniques to linearize time series which present nonlinearities; (2) the stability of the linear projection giving the components estimators under non-linear misspeci®cations; (3) the capacity of the WK ®lter to preserve the linearity in some components and the non-linearities in others.