The purpose of the manuscript is to outline and demonstrate problems with the use of the HP filter, and to propose an alternative strategy for inferring cyclical behavior from a time series that features seasonal, trend, cyclical and noise components. The main innovation of the alternative strategy involves augmenting the series in question with forecasts and backcasts obtained from an ARIMA model, and then applying the HP filter to the augmented series. Comparisons presented in the paper using artificial and actual data demonstrate the superiority of the alternative strategy.
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From the reviews:
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."
1 Introduction and Brief Summary.- 2 A Brief Review of Applied Time Series Analysis.- 2.1 Some Basic Concepts.- 2.2 Stochastic Processes and Stationarity.- 2.3 Differencing.- 2.4 Linear Stationary Process, Wold Representation. and Auto-correlation Function.- 2.5 The Spectrum.- 2.6 Linear Filters and Their Squared Gain.- 3 ARIMA Models and Signal Extraction.- 3.1 ARIMA Models.- 3.2 Modeling Strategy, Diagnostics and Inference.- 3.2.1 Identification.- 3.2.2 Estimation and Diagnostics.- 3.2.3 Inference.- 3.2.4 A Particular Class of Models.- 3.3 Preadjustment.- 3.4 Unobserved Components and Signal Extraction.- 3.5 ARIMA-Model-Based Decomposition of a Time Series.- 3.6 Short-Term and Long-Term Trends.- 4 Detrending and the Hodrick-Prescott Filter.- 4.1 The Hodrick-Prescott Filter: Equivalent Representations.- 4.2 Basic Characteristics of the Hodrick-Prescott Filter.- 4.3 Some Criticisms and Discussion of the Hodrick-Prescott Filter.- 4.4 The Hodrick-Prescott Filter as a Wiener-Kolmogorov Filter.- 4.4.1 An Alternative Representation.- 4.4.2 Derivation of the Filter.- 4.4.3 The Algorithm.- 4.4.4 A Note on Computation.- 5 Some Basic Limitations of the Hodrick-Prescott Filter.- 5.1 Endpoint Estimation and Revisions.- 5.1.1 Preliminary Estimation and Revisions.- 5.1.2 An Example.- 5.2 Spurious Results.- 5.2.1 Spurious Crosscorrelation.- 5.2.2 Spurious Autocorrelation; Calibration.- 5.2.3 Spurious Periodic Cycle.- 5.3 Noisy Cyclical Signal.- 6 Improving the Hodrick-Prescott Filter.- 6.1 Reducing Revisions.- 6.2 Improving the Cyclical Signal.- 7 Hodrick-Prescott Filtering Within a Model-Based Approach.- 7.1 A Simple Model-Based Algorithm.- 7.2 A Complete Model-Based Method; Spuriousness Reconsidered.- 7.3 Some Comments on Model-Based Diagnostics and Inference.- 7.4 MMSE Estimation of the Cycle: A Paradox.- References.- Author Index.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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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. Codice articolo 343ZB
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Paperback. Condizione: new. Paperback. The purpose of the manuscript is to outline and demonstrate problems with the use of the HP filter, and to propose an alternative strategy for inferring cyclical behavior from a time series that features seasonal, trend, cyclical and noise components. The main innovation of the alternative strategy involves augmenting the series in question with forecasts and backcasts obtained from an ARIMA model, and then applying the HP filter to the augmented series. Comparisons presented in the paper using artificial and actual data demonstrate the superiority of the alternative strategy. This book will be useful to economists and analysts in government and financial/commercial companies who routinely monitor the state of the economic cycle, and who produce short-term forecasts. It will also be of interest to academics who do business cycle research, and who need to extract a measure of the cycle from the data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780387951126
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Condizione: 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. Codice articolo 345050
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PAPERBACK. Condizione: Very Good. 0387951121 Very Good++; Softcover; 2000, Springer-Verlag Publishing; Covers still glossy; Pages bright & unmarked; Tight binding with straight spine; Tan and black covers with title in black lettering; 190 pages; "Measuring Business Cycles in Economic Time Series (Lecture Notes in Statistics)," by Regina Kaiser & Agustin Maravall. Codice articolo SKU-E8309308201
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Taschenbuch. Condizione: 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. Codice articolo 9780387951126
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