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E-raamat: Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation

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Seasonal Adjustment Methods and Real Time Trend-Cycle EstimationSuurem pilt
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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. 

 

Arvustused

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)

1 Introduction
1(28)
1.1 Book Structure
1(1)
1.2 Part I: Seasonal Adjustment Methods
2(19)
1.2.1 Smoothing Linear Seasonal Adjustment Methods
3(6)
1.2.2 ARIMA Model-Based Seasonal Adjustment Method
9(5)
1.2.3 Structural Time Series Models
14(7)
1.3 Part II: Real Time Trend-Cycle Estimation
21(8)
References
26(3)
2 Time Series Components
29(32)
2.1 Time Series Decomposition Models
30(3)
2.2 The Secular or Long-Term Trend
33(3)
2.2.1 Deterministic Trend Models
34(1)
2.2.2 Stochastic Trends
34(2)
2.3 The Business Cycle
36(3)
2.3.1 Deterministic and Stochastic Models for the Business Cycle
39(1)
2.4 The Seasonal Variations
39(8)
2.4.1 Seasonal Adjustment Methods
41(6)
2.5 Calendar Variations
47(5)
2.5.1 The Moving Holiday Component
47(2)
2.5.2 The Trading Day Component
49(3)
2.6 The Irregular Component
52(9)
2.6.1 Redistribution of Outliers and Strikes
52(1)
2.6.2 Models for the Irregulars and Outliers
53(3)
References
56(5)
Part I Seasonal Adjustment Methods
3 Seasonal Adjustment: Meaning, Purpose, and Methods
61(18)
3.1 Seasonality, Its Causes and Characteristics
61(2)
3.2 The Economic Significance of Seasonality and the Need for Seasonally Adjusted Series
63(2)
3.3 Basic Assumptions of Main Seasonal Adjustment Methods
65(14)
3.3.1 Regression Methods
66(5)
3.3.2 Stochastic Model-Based Methods
71(1)
3.3.3 Linear Smoothing Methods
72(4)
References
76(3)
4 Linear Filters Seasonal Adjustment Methods: Census Method II and Its Variants
79(36)
4.1 Introduction
79(4)
4.1.1 Main Steps to Produce a Seasonally Adjusted Series
81(2)
4.2 Basic Properties of the Two-Sided Linear Smoothing Filters of Census Method II-X11 Variant
83(2)
4.2.1 The Centered 12 Months Moving Average
83(1)
4.2.2 The 9-, 13-, and 23-Term Henderson Moving Averages
84(1)
4.2.3 The Weighted 5-Term (3 x 3) and 7-Term (3 × 5) Moving Averages
84(1)
4.3 Basic Properties of the One-Sided Linear Smoothing Filters of Census Method II-X11 Variant
85(1)
4.4 The X11ARIMA Method
86(9)
4.4.1 General Outline and Basic Assumptions
86(2)
4.4.2 The Forecasting Filters of ARIMA Models and Their Properties
88(3)
4.4.3 Other Main Improvements Incorporated into the Automated Version of X11ARIMA
91(4)
4.5 The X12ARIMA Method
95(5)
4.5.1 General Outline
95(2)
4.5.2 The General RegARIMA Model
97(3)
4.6 Illustrative Example: X12ARIMA Seasonal Adjustment of the US NODG Series
100(15)
4.6.1 Input: Specification File
101(2)
4.6.2 Testing for the Presence of Identifiable Seasonality
103(2)
4.6.3 Pre-processing
105(5)
4.6.4 Decomposition
110(3)
References
113(2)
5 Seasonal Adjustment Based on ARIMA Model Decomposition: TRAMO-SEATS
115(32)
5.1 TRAMO: Time Series Regression with ARIMA Noise, Missing Observations, and Outliers
116(5)
5.2 SEATS: Signal Extraction in ARIMA Time Series
121(5)
5.3 Illustrative Example: TRAMO-SEATS Seasonal Adjustment of the US NODG Series
126(21)
5.3.1 Input: Specifications
127(1)
5.3.2 Testing for the Presence of Identifiable Seasonality
128(1)
5.3.3 Pre-processing
128(8)
5.3.4 Decomposition
136(8)
References
144(3)
6 Seasonal Adjustment Based on Structural Time Series Models
147(20)
6.1 Structural Time Series Models
148(5)
6.1.1 Trend Models
148(2)
6.1.2 The Cyclical Component
150(1)
6.1.3 Seasonality
151(2)
6.1.4 Regression Component
153(1)
6.2 Linear State Space Models
153(6)
6.2.1 The Kalman Filter
155(1)
6.2.2 Likelihood Estimation
156(2)
6.2.3 Diagnostic Checking
158(1)
6.3 Illustrative Example: Analysis of the US Unemployment Rate for Males Using STAMP
159(8)
References
163(4)
Part II Trend-Cycle Estimation
7 Trend-Cycle Estimation
167(30)
7.1 Deterministic Global Trend Models
168(2)
7.2 Stochastic Global Trend Models
170(8)
7.2.1 TRAMO-SEATS Trend Models
172(3)
7.2.2 STAMP Trend Models
175(3)
7.3 Stochastic Local Trend-Cycle Models
178(13)
7.3.1 Locally Weighted Regression Smoother (LOESS)
180(1)
7.3.2 Henderson Smoothing Filter
181(3)
7.3.3 Gaussian Kernel Smoother
184(1)
7.3.4 Cubic Smoothing Spline
185(3)
7.3.5 Theoretical Properties of Symmetric and Asymmetric Linear Trend-Cycle Filters
188(3)
7.4 Illustrative Results
191(6)
References
193(4)
8 Further Developments on the Henderson Trend-Cycle Filter
197(28)
8.1 The Nonlinear Dagum Filter (NLDF)
198(3)
8.2 The Cascade Linear Filter
201(9)
8.2.1 The Symmetric Linear Filter
202(3)
8.2.2 The Asymmetric Linear Filter
205(5)
8.3 The Henderson Filter in the Reproducing Hilbert Space (RKHS)
210(15)
8.3.1 Linear Filters in Reproducing Kernel Hilbert Spaces
211(4)
8.3.2 The Symmetric Henderson Smoother and Its Kernel Representation
215(5)
8.3.3 Asymmetric Henderson Smoothers and Their Kernel Representations
220(2)
References
222(3)
9 A Unified View of Trend-Cycle Predictors in Reproducing Kernel Hilbert Spaces (RKHS)
225(18)
9.1 Nonparametric Estimators in RKHS
226(9)
9.1.1 Polynomial Kernel Regression
230(1)
9.1.2 Smoothing Spline Regression
231(4)
9.2 Boundary Behavior
235(8)
9.2.1 Empirical Evaluation
237(3)
References
240(3)
10 Real Time Trend-Cycle Prediction
243(20)
10.1 Asymmetric Filters and RKHS
245(5)
10.1.1 Properties of the Asymmetric Filters
249(1)
10.2 Optimal Bandwidth Selection
250(5)
10.3 Empirical Application
255(8)
10.3.1 Reduction of Revision Size in Real Time Trend-Cycle Estimates
256(2)
10.3.2 Turning Point Detection
258(4)
References
262(1)
11 The Effect of Seasonal Adjustment on Real-Time Trend-Cycle Prediction
263(16)
11.1 Seasonal Adjustment Methods
264(6)
11.1.1 X12ARIMA
264(4)
11.1.2 TRAMO-SEATS
268(2)
11.2 Trend-Cycle Prediction in Reproducing Kernel Hilbert Space (RKHS)
270(2)
11.3 Empirical Application
272(7)
11.3.1 Reduction of Revisions in Real Time Trend-Cycle Estimates
275(1)
11.3.2 Turning Point Detection
275(2)
References
277(2)
Glossary 279
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 worlds statistical agencies. In 1994, she jointly developed a benchmarking regression method that is currently used by Statistics Canada and otheragencies 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 linear models.
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