Short and long memory unobserved components in hydrological time series

In this paper a semiparametric approach is introduced to decompose an ARFIMA model in the long memory and short memory unobserved components. The procedure is based on the DECOMEL method which produces a statistical decomposition by minimizing the Euclidean distance between the spectrum of the aggregated series and the sum of the parametric spectra of the components. The extension to long memory stationary models is achieved defining an approximate model where the fractional operator is replaced by the ratio of two polynomials of order one. The feasibility and performance of the proposed procedure are discussed through a case study. © 2006.