Estela Bee Dagum, Silvia Bianconcini

eBook, PDF

Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation (eBook, PDF)

Beschreibung

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.

Technische Details

• Geräte: PC
• ohne Kopierschutz
• Größe: 4.71MB

Barrierefreiheit

• Text-to-Speech

Autor

Estela Bee Dagum is currently a Research Professor of the Department of Statistical Sciences of the University of Bologna, Italy where she was a Full Professor for 10 years until 2007 (appointed by Chiara Fama, an Italian system for appointing internationally recognized scientists of the very highest caliber). From 2007 until December 2009 she was appointed as Alumna of the Business Survey and Methodology Division at Statistics Canada to serve as a consultant on time series issues, particularly on linkage, benchmarking, trend and seasonal adjustment. Previously, Estelle Bee Dagum was Director of the Time Series Research and Analysis Centre of Statistics Canada where she worked for 21 years (1972-1993). In 1980, she developed the X11ARIMA seasonal adjustment method, later modified to X12ARIMA, which is currently used by most of the world's statistical agencies. In 1994, she jointly developed a benchmarking regression method that is currently used by Statistics Canada and otheragencies for benchmarking, interpolation, linkage and reconciliation of time series systems. Estelle Bee Dagum has served as a consultant to a large number of governments and private entities, published 19 books on time series analysis related topics, and more than 150 papers in leading scientific and statistical journals.

Silvia Bianconcini is an Associate Professor at the Department of Statistical Sciences, University of Bologna, where she received her PhD on Statistical Methodology for the Scientific Research. Her main research interests are time series analysis with an emphasis on signal extraction, longitudinal data analysis based on latent variable models, and statistical inference of generalized linear models.

Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 283
• Erscheinungstermin: 20. Juni 2016
• Englisch
• ISBN-13: 9783319318226
• Artikelnr.: 46934599

Rezensionen

"Each chapter is completed by a list of the most recent references, and the book contains a list of acronyms and glossary, which facilitates reading throughout multiple terms conventional in this field. For professionals and students dealing with time series data the monograph can be very useful as a guide in the wide-ranging area of modern modeling and forecasting methods and software." (Stan Lipovetsky, Technometrics, Vol. 59 (2), April, 2017)