Seasonal adjustment methods and real time trend-cycle estimation
- 作者： Dagum, Estela Bee, author.
- 其他作者：
- 其他題名：
- Statistics for social and behavioral sciences.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Statistics for social and behavioral sciences,
- 主題： Seasonal variations (Economics) , Estimation theory. , Statistics. , Statistics for Business/Economics/Mathematical Finance/Insurance. , Statistical Theory and Methods. , Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. , Macroeconomics/Monetary Economics/Financial Economics. , Probability Theory and Stochastic Processes. , Econometrics.
- ISBN： 9783319318226 (electronic bk.) 、 9783319318202 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 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.
- 摘要註: 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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讀者標籤：
- 系統號： 005363868 | 機讀編目格式
館藏資訊
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.