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Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series
Time series play a crucial role in modern economies at all levels of activity and are used by decision makers to plan for a better future. Before publication time series are subject to statistical adjustments and this is the first statistical book to systematically deal with the methods most often applied for such adjustments. Regression-based models are emphasized because of their clarity, ease of application, and superior results. Each topic is illustrated with real case examples. In order to facilitate understanding of their properties and limitations of the methods discussed a real data example is followed throughout the book.
Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation
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 adj...
New Developments in the X-11-ARIMA
"Developments related with the seasonal adjustment function include (i) estimation of Easter effect; (ii) estimation of stochastic trading-day variations; (iii) new replacement of extreme values; (iv) increasing the accuracy of the end-weights of the five Henderson trend-cycle filters; and (v) new diagnostic tools. The main purpose of this paper is to provide a summary of each one of these major developments and their impact on the two corresponding basic functions of X-11-ARIMA"--Abstract.
