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Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation. 1st ed. 2016

種類:
電子ブック
責任表示:
by Estela Bee Dagum, Silvia Bianconcini
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2016
著者名:
シリーズ名:
Statistics for Social and Behavioral Sciences ;
ISBN:
9783319318226 [3319318225]  CiNii Books  Calil
注記:
Introduction -- Time Series Components -- Part I: Seasonal Adjustment Methods -- Seasonal Adjustment: Meaning, Purpose and Methods -- Linear Filters Seasonal Adjustment Methods: Census Method II and its Variants -- Seasonal Adjustment Based on ARIMA Decomposition: TRAMO-SEATS.- Seasonal Adjustment Based on Structural Time Series Models -- Part II: Trend-Cycle Estimation.- Trend-Cycle Estimation.- Further Developments on the Henderson Trend-Cycle Filter.- A Unified View of Trend-Cycle Predictors in Reproducing Kernel Hilbert Spaces (RKHS).- Real Time Trend-Cycle Prediction.- The Effect of Seasonal Adjustment on Real-Time Trend-Cycle Prediction -- Glossary.
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, transporta
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