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Seasonal adjustment methods and real...

Dagum, Estela Bee.

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Seasonal adjustment methods and real time trend-cycle estimation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Seasonal adjustment methods and real time trend-cycle estimation/ by Estela Bee Dagum, Silvia Bianconcini.
作者:	Dagum, Estela Bee.
其他作者:	Bianconcini, Silvia.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xvi, 283 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Seasonal variations (Economics) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-31822-6
ISBN:	9783319318226

Seasonal adjustment methods and real time trend-cycle estimation
Dagum, Estela Bee.

Seasonal adjustment methods and real time trend-cycle estimation[electronic resource] /by Estela Bee Dagum, Silvia Bianconcini. - Cham :Springer International Publishing :2016. - xvi, 283 p. :ill. (some col.), digital ;24 cm. - Statistics for social and behavioral sciences,2199-7357. - Statistics for social and behavioral sciences..

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.

ISBN: 9783319318226

Standard No.: 10.1007/978-3-319-31822-6doiSubjects--Topical Terms:

2016020
Seasonal variations (Economics)

LC Class. No.: HB137

Dewey Class. No.: 338.54

Seasonal adjustment methods and real time trend-cycle estimation
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505 0 $a 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.
520 $a 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.
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