| 书目名称 | Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation | | 编辑 | Estela Bee Dagum,Silvia Bianconcini | | 视频video | | | 概述 | Collates seasonal adjustment methods jointly with real time trend-cycle estimation.Develops estimation methods widely used by national statistical agencies.Facilitates understanding employing real-dat | | 丛书名称 | Statistics for Social and Behavioral Sciences | | 图书封面 |  | | 描述 | .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 othe | | 出版日期 | Book 2016 | | 关键词 | 62G08, 62M10, 62P20, 62P25; STAMP; TRAMO-SEATS; X12ARIMA; real time trend-cycle prediction; seasonal adju | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-31822-6 | | isbn_softcover | 978-3-319-81127-7 | | isbn_ebook | 978-3-319-31822-6Series ISSN 2199-7357 Series E-ISSN 2199-7365 | | issn_series | 2199-7357 | | copyright | Springer International Publishing Switzerland 2016 |
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发表于 2025-3-21 16:52:47
