Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation / Statistics for Social and Behavioral Sciences (PDF)
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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.
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.
- Autoren: Estela Bee Dagum , Silvia Bianconcini
- 2016, 1st ed. 2016, 283 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319318225
- ISBN-13: 9783319318226
- Erscheinungsdatum: 20.06.2016
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- Größe: 4.60 MB
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