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Abstract

In this paper, the Singular Spectrum Analysis SSA, a relatively new tool originated in natural sciences, for orthogonal decomposition of time series, is presented and applied in the European real, seasonally unadjusted quarterly GDP for the period 1995 - 2010. SSA is suitable for short and noisy time series, properties that characterize many macroeconomic time series. In this paper, I decompose the GDP in trend, cycle, seasonals and noise components. There are significant similarities but also some differences between the SSA-based filter and the other well-known macroeconomic filters. These differences are shown here by means of correlation matrices and spectral measures. Although SSA is a method that only very recently has been introduced in macroeconomics, its use in the natural sciences for more than three decades, makes it a serious candidate for tackling macroeconomic issues such as filtering, denoising and smoothing.



Item Type: MPRA Paper -

Original Title: Decomposition of the European GDP based on Singular Spectrum Analysis-

Language: English-

Keywords: Macroeconomics, economic fluctuations, business cycle, dynamical systems, spectral methods, singular spectrum analysis.-

Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: GeneralC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: GeneralC - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special TopicsC - Mathematical and Quantitative Methods > C5 - Econometric ModelingE - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and CyclesE - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; CyclesE - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications-





Autor: Leon, Costas

Fuente: https://mpra.ub.uni-muenchen.de/65812/







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