The main purpose of this paper is to introduce a linear transformation, called t, and to derive its algebraic properties by means of permutation matrices that represent it. To demonstrate the importance of the t-transformation for the estimation of latent variables in time series decomposition, we obtain a general expression for smoothing matrices characterized by symmetric and asymmetric weighting systems. We show that the submatrix of the symmetric weights (to be applied to central observations) is t-invariant whereas the submatrices of the asymmetric weights (to be applied to initial and final observations) are the t-transform of each other. By virtue of this relation, the properties of the t-transformation provide useful information on the smoothing of time series data. Finally, we illustrate the role of the t-transformation on the weighting systems of several smoothers often applied for trend-cycle estimation, such as the locally weighted regression smoother (loess), the cubic smoothing spline, the Gaussian kernel and the 13-term trend-cycle Henderson filter.
A linear transformation and its properties with special applications in time series filtering / E.B. Dagum; A. Luati. - In: LINEAR ALGEBRA AND ITS APPLICATIONS. - ISSN 0024-3795. - STAMPA. - 338:(2004), pp. 107-117. [10.1016/S0024-3795(03)00397-5]
A linear transformation and its properties with special applications in time series filtering
DAGUM, ESTELLE BEE;LUATI, ALESSANDRA
2004
Abstract
The main purpose of this paper is to introduce a linear transformation, called t, and to derive its algebraic properties by means of permutation matrices that represent it. To demonstrate the importance of the t-transformation for the estimation of latent variables in time series decomposition, we obtain a general expression for smoothing matrices characterized by symmetric and asymmetric weighting systems. We show that the submatrix of the symmetric weights (to be applied to central observations) is t-invariant whereas the submatrices of the asymmetric weights (to be applied to initial and final observations) are the t-transform of each other. By virtue of this relation, the properties of the t-transformation provide useful information on the smoothing of time series data. Finally, we illustrate the role of the t-transformation on the weighting systems of several smoothers often applied for trend-cycle estimation, such as the locally weighted regression smoother (loess), the cubic smoothing spline, the Gaussian kernel and the 13-term trend-cycle Henderson filter.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.