This book outlines and demonstrates problems with the use of the HP filter, and proposes an alternative strategy for inferring cyclical behavior from a time series featuring seasonal, trend, cyclical and noise components. The main innovation of the alternative strategy involves augmenting the series forecasts and back-casts obtained from an ARIMA model, and then applying the HP filter to the augmented series. Comparisons presented using artificial and actual data demonstrate the superiority of the alternative strategy.
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MATHEMATICAL REVIEWS
"Altogether this book is more on the mathematical side, it is well written following the same idea throughout and contains many exercises which complete the different topics. The text concentrates on the approach of the authors...I enjoyed reading this nicely written book which can certainly be recommended to all mathematically oriented statisticians interested in the subject."
This book outlines and demonstrates problems with the use of the HP filter, and proposes an alternative strategy for inferring cyclical behavior from a time series featuring seasonal, trend, cyclical and noise components. The main innovation of the alternative strategy involves augmenting the series forecasts and back-casts obtained from an ARIMA model, and then applying the HP filter to the augmented series. Comparisons presented using artificial and actual data demonstrate the superiority of the alternative strategy.
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Softcover reprint of the original 1st ed. 2001. 16 x 24 cm. VIII, 190 S. VIII, 190 p. 1 illus. in color. Softcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. (Lecture Notes in Statistics). Sprache: Englisch. Bestandsnummer des Verkäufers 343ZB
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Zustand: Gut. 204 Seiten nice book ex Library Sprache: Englisch Gewicht in Gramm: 279 23,0 x 15,2 x 0,6 cm, Taschenbuch Auflage: Softcover reprint of the original 1st ed. 2001. Bestandsnummer des Verkäufers 345050
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book outlines and demonstrates problems with the use of the HP filter, and proposes an alternative strategy for inferring cyclical behavior from a time series featuring seasonal, trend, cyclical and noise components. The main innovation of the alter. Bestandsnummer des Verkäufers 5912339
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Taschenbuch. Zustand: Neu. Neuware -lengths, that could not be captured with univariate linear filters. Exam ples of research in both directions can be found in Sims (1977), Lahiri and Moore (1991), Stock and Watson (1993), and Hamilton (1994) and (1989). Although the first approach is known to present serious limitations,the new and more sophisticated methods developed in the second approach (most notably, multivariate and nonlinear extensions) are at an early stage, and have proved still unreliable, displaying poor behavior when moving away from the sample period . Despite the fact that business cycle estimation is basic to the conduct of macroeconomic policy and to monitoring of the economy, many decades of attention have shown that formal modeling of economic cycles is a frustrating issue. As Baxter and King (1999) point out, we still face at present the same basic question 'as did Burns and Mitchell fifty years ago: how should one isolate the cyclical component of an eco nomic time series In particular, how should one separate business-cycle elements from slowly evolving secular trends, and rapidly varying seasonal or irregular components ' Be that as it may, it is a fact that measuring (in some way) the busi ness cycle is an actual pressing need of economists, in particular of those related to the functioning of policy-making agencies and institutions, and of applied macroeconomic research.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 204 pp. Englisch. Bestandsnummer des Verkäufers 9780387951126
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book outlines and demonstrates problems with the use of the HP filter, and proposes an alternative strategy for inferring cyclical behavior from a time series featuring seasonal, trend, cyclical and noise components. The main innovation of the alternative strategy involves augmenting the series forecasts and back-casts obtained from an ARIMA model, and then applying the HP filter to the augmented series. Comparisons presented using artificial and actual data demonstrate the superiority of the alternative strategy. 204 pp. Englisch. Bestandsnummer des Verkäufers 9780387951126
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - lengths, that could not be captured with univariate linear filters. Exam ples of research in both directions can be found in Sims (1977), Lahiri and Moore (1991), Stock and Watson (1993), and Hamilton (1994) and (1989). Although the first approach is known to present serious limitations,the new and more sophisticated methods developed in the second approach (most notably, multivariate and nonlinear extensions) are at an early stage, and have proved still unreliable, displaying poor behavior when moving away from the sample period . Despite the fact that business cycle estimation is basic to the conduct of macroeconomic policy and to monitoring of the economy, many decades of attention have shown that formal modeling of economic cycles is a frustrating issue. As Baxter and King (1999) point out, we still face at present the same basic question 'as did Burns and Mitchell fifty years ago: how should one isolate the cyclical component of an eco nomic time series In particular, how should one separate business-cycle elements from slowly evolving secular trends, and rapidly varying seasonal or irregular components ' Be that as it may, it is a fact that measuring (in some way) the busi ness cycle is an actual pressing need of economists, in particular of those related to the functioning of policy-making agencies and institutions, and of applied macroeconomic research. Bestandsnummer des Verkäufers 9780387951126
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