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Anotace knihy

This book explores widely used seasonal adjustment methods and recent developments in real time trend-cycle estimation. It discusses in detail the properties and limitations of X12ARIMA, TRAMO-SEATS and STAMP - the main seasonal adjustment methods used by statistical agencies. Several real-world cases illustrate each method and real data examples can be followed throughout the text. The trend-cycle estimation is presented using nonparametric techniques based on moving averages, linear filters and reproducing kernel Hilbert spaces, taking recent advances into account. The book provides a systematical treatment of results that to date have been scattered throughout the literature. Seasonal adjustment and real time trend-cycle prediction play an essential part at all levels of activity in modern economies. They are used by governments to counteract cyclical recessions, by central banks to control inflation, by decision makers for better modeling and planning and by hospitals, manufacturers, builders, transportation, and consumers in general to decide on appropriate action. This book appeals to practitioners in government institutions, finance and business, macroeconomists, and other professionals who use economic data as well as academic researchers in time series analysis, seasonal adjustment methods, filtering and signal extraction. It is also useful for graduate and final-year undergraduate courses in econometrics and time series with a good understanding of linear regression and matrix algebra, as well as ARIMA modelling.

Parametry knihy

Zařazení knihy Knihy v angličtině Society & social sciences Sociology & anthropology Sociology

• Plný název: Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation
• Autor: Estela Bee Dagum, Silvia Bianconcini
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 283
• EAN: 9783319811277
• ISBN: 9783319811277
• ID: 19703601
• Nakladatelství: Springer International Publishing AG
• Hmotnost: 466 g
• Rozměry: 156 × 233 × 20 mm
• Datum vydání: 31. May 2018

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