Time Series Decomposition and Seasonal Adjustment
暫譯: 時間序列分解與季節調整
Zong, Ping
- 出版商: CRC
- 出版日期: 2026-03-31
- 售價: $5,350
- 貴賓價: 9.5 折 $5,082
- 語言: 英文
- 頁數: 362
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1041204612
- ISBN-13: 9781041204619
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相關分類:
Data-mining
尚未上市，無法訂購
商品描述
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 that combine machine learning with traditional filters, Bayesian State Space approaches, and adaptive methods such as 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.
商品描述(中文翻譯)
本書深入探討時間序列分解和季節調整，重點介紹 X-13ARIMA-SEATS 和 TRAMO-SEATS 方法。季節調整能夠消除經濟指標（例如 GDP、CPI）中的扭曲現象，如季節波動和假日效應，使得對潛在趨勢、循環和衝擊的洞察更加清晰。這些工具對於健全的政策制定、準確的預測和可靠的國際比較至關重要。
X-13ARIMA-SEATS 由美國人口普查局開發，結合了經驗移動平均濾波器與 ARIMA/regARIMA 建模，以處理異常值、日曆效應和端點問題。TRAMO-SEATS 則由西班牙銀行創建，採用基於模型的策略：TRAMO 使用 ARIMA 模型對數據進行預調整，而 SEATS 則應用信號提取來分解組件。X-13ARIMA-SEATS 在穩定的季節模式下表現優異，而 TRAMO-SEATS 則為複雜的假日結構提供嚴謹的解決方案。
本書還探討了現代挑戰，包括 COVID-19 帶來的結構性變化、高頻數據的多重季節性以及對實時調整的需求。它回顧了創新，如結合機器學習與傳統濾波器的混合模型、貝葉斯狀態空間方法，以及如卡爾曼濾波器等自適應方法。
本書旨在為學生、研究人員、國家統計機構、中央銀行和金融機構的工作人員提供方法論和實用工具，以應對不斷變化的經濟數據環境。
作者簡介
Dr Ping Zong holds a bachelor's and master's degree in economics from Fudan University, China 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 research fellow at the University of Newcastle Upon Tyne, 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, as well as 15 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.
作者簡介(中文翻譯)
宗平博士擁有中國復旦大學的經濟學學士及碩士學位，以及英國貝爾法斯特女王大學的經濟學博士學位。他曾擔任貝爾法斯特女王大學的研究員、英國學術院K.C. Wong研究員於紐卡斯爾大學，以及埃塞克斯大學的高級研究員。從2007年到2020年退休，他在英國國家統計局擔任高級研究官及方法學家，專注於統計方法論。
他的主要研究興趣包括參數估計、經濟預測的計量經濟模型以及時間序列分析，特別關注統計和計量經濟方法論。
宗博士已發表25篇學術文章於學術期刊，並著作15本書籍，包括單作者作品及合作卷。他最近的出版物包括可銷售盈餘供應的經濟學（2018年由Routledge再版）和計量經濟學的藝術與科學（2022年由Routledge出版）。他也受邀擔任多個學術期刊及出版社的同行評審。
