Signal Extraction : Efficient Estimation, ‘Unit Root'-Tests and Early Detection of Turning Points /
by Marc Wildi.
- xI, 279 páginas 80 ilustraciones recurso en línea.
- Lecture Notes in Economics and Mathematical Systems, 547 0075-8442 ; .
Springer eBooks
Theory -- Model-Based Approaches -- QMP-ZPC Filters -- The Periodogram -- Direct Filter Approach (DFA) -- Finite Sample Problems and Regularity -- Empirical Results -- Empirical Comparisons : Mean Square Performance -- Empirical Comparisons : Turning Point Detection -- Conclusion.
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.