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Adaptive time series filters to smooth and forecast economic variables
Author(s) -
Pervukhina Elena,
Emmenegger JeanFrançois
Publication year - 2007
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.200700849
Subject(s) - series (stratigraphy) , divergence (linguistics) , filter (signal processing) , differentiable function , measure (data warehouse) , matrix (chemical analysis) , econometrics , mathematics , time series , hodrick–prescott filter , state (computer science) , state vector , basis (linear algebra) , statistics , computer science , algorithm , data mining , economics , mathematical analysis , business cycle , philosophy , materials science , linguistics , keynesian economics , composite material , biology , paleontology , geometry , computer vision , classical mechanics , physics
Four monthly Ukrainian trucking industry time series are investigated within period of January 2003 to February 2007. The AR(1) models are used to filter and predict real values of the series. The problems are solved on the basis of a differentiable matrix functional, defined on the coefficient matrix of the filter. The functional presents an information measure of the distribution parameters of the vector state and its tentative estimations and is well known as Kullback information divergence. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)