Time Series Decomposition and Seasonal Adjustment - Zong, Ping
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This book provides an in-depth examination of time series decomposition and seasonal adjustment, focusing on the X-13ARIMA-SEATS and TRAMO-SEATS methods. Seasonal adjustment removes distortions such as seasonal fluctuations and holiday effects from economic indicators (eg, GDP, CPI), enabling clearer insights into underlying trends, cycles, and shocks. These tools are vital for sound policymaking, accurate forecasting, and reliable international comparisons. X-13ARIMA-SEATS, developed by the U.S. Census Bureau, combines empirical moving average filters with ARIMA/regARIMA modelling to handle…mehr

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Produktbeschreibung
This book provides an in-depth examination of time series decomposition and seasonal adjustment, focusing on the X-13ARIMA-SEATS and TRAMO-SEATS methods. Seasonal adjustment removes distortions such as seasonal fluctuations and holiday effects from economic indicators (eg, GDP, CPI), enabling clearer insights into underlying trends, cycles, and shocks. These tools are vital for sound policymaking, accurate forecasting, and reliable international comparisons. X-13ARIMA-SEATS, developed by the U.S. Census Bureau, combines empirical moving average filters with ARIMA/regARIMA modelling to handle outliers, calendar effects, and endpoint issues. TRAMO-SEATS, created by the Bank of Spain, uses a model-based strategy: TRAMO pre-adjusts data with ARIMA models, while SEATS applies signal extraction to decompose components. X-13ARIMA-SEATS excels with stable seasonal patterns, while TRAMO-SEATS provides rigorous solutions for complex holiday structures. The book also examines modern challenges, including structural breaks from COVID-19, high-frequency data with multiple seasonalities, and the demand for real-time adjustments. It reviews innovations such as hybrid models combining machine learning with traditional filters, Bayesian state-space approaches, and adaptive methods like Kalman filters. Intended for students, researchers, staff at national statistical agencies, central banks, and financial institutions, the book equips readers with methodological and practical tools to navigate evolving economic data landscapes.
Autorenporträt
Dr. Zong Ping holds a Bachelor's and Master's degree in Economics from Fudan University, and a Ph.D. in Economics from Queen's University Belfast, UK. He has served as a Research Fellow at Queen's University Belfast, a British Academy K.C. Wong Fellow at Newcastle University, and a Senior Research Fellow at the University of Essex. From 2007 until his retirement in 2020, he worked as a Senior Research Officer and Methodologist at the UK Office for National Statistics, specialising in statistical methodology. His primary research interests include parameter estimation, econometric modelling for economic forecasting, and time series analysis, with a particular focus on statistical and econometric methodology. Dr. Zong has authored 25 academic articles published in scholarly journals, along with 14 books, including single-author works and collaborative volumes. His recent publications include Economics of Marketable Surplus Supply (reprinted by Routledge in 2018) and The Art and Science of Econometrics (published by Routledge in 2022). He has also been invited to serve as a peer reviewer for several academic journals and publishing houses.