Overview

The book provides deep insights into the signal extraction problem - especially at the boundary of a sample, where asymmetric filters must be used - and how to solve it optimally. The traditional model-based approach (TRAMO/SEATS or X-12-ARIMA) is an inefficient estimation method because it relies on one-step ahead forecasting performances (of a model) whereas the signal extraction problem implicitly requires good multi-step ahead forecasts also. Unit roots are important properties of the input signal because they generate a set of constraints for the best extraction filter. Since traditional tests essentially rely on one-step ahead forecasting performances, new tests are presented here which implicitly account for multi-step ahead forecasting performances too. The gain in efficiency obtained by the new estimation method is analyzed in great detail, using simulated data as well as 'real world' time series.

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9783540803577

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<p>From the reviews: <p><p> The aim of the author is to describe established procedures which are implemented in widely used software packages. The book can be of great interest for all specialists working in the area of nonlinear systems state and parameter estimation and identification. It will be of significant benefit for time series estimation and prediction in many applications. (Tzvetan Semerdjiev, Zentralblatt MATH, Vol. 1053, 2005)

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