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＜電子ブック＞
Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation / by Estela Bee Dagum, Silvia Bianconcini
(Statistics for Social and Behavioral Sciences. ISSN:21997365)

版	1st ed. 2016.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2016
本文言語	英語
大きさ	XVI, 283 p. 52 illus., 10 illus. in color : online resource
著者標目	*Bee Dagum, Estela author Bianconcini, Silvia author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Social sciences -- Statistical methods  全ての件名で検索 LCSH:Macroeconomics LCSH:Probabilities LCSH:Econometrics FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Statistical Theory and Methods FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Macroeconomics and Monetary Economics FREE:Probability Theory FREE:Econometrics
一般注記	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, 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 HTTP:URL=https://doi.org/10.1007/978-3-319-31822-6

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