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.
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 other agencies 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
“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)