In Defence of Model-Based Seasonal Adjustment PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download In Defence of Model-Based Seasonal Adjustment PDF full book. Access full book title In Defence of Model-Based Seasonal Adjustment by Imad A. Moosa. Download full books in PDF and EPUB format.
Author: Imad A. Moosa Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
It is argued that the X-11 seasonal adjustment procedure suffers from severe drawbacks, and so it should be abandoned in favour of model-based seasonal adjustment. Furthermore, it is argued that Harvey's structural time series model is superior to the conventional seasonal ARIMA models for the purpose of model-based seasonal adjustment. It is shown, with the help of a large number of Australian time series, that the nature of seasonality differs from one series to another, and this is why model selection is crucial for seasonal adjustment. It is further shown that model-based seasonal adjustment could produce results that are significantly different from those obtained by applying the X-11 procedure. Since the X-11 procedure is not based on an explicit model and in view of its other serious drawbacks, it is concluded that the procedure should be abandoned in favour of model-based seasonal adjustment.
Author: Imad A. Moosa Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
It is argued that the X-11 seasonal adjustment procedure suffers from severe drawbacks, and so it should be abandoned in favour of model-based seasonal adjustment. Furthermore, it is argued that Harvey's structural time series model is superior to the conventional seasonal ARIMA models for the purpose of model-based seasonal adjustment. It is shown, with the help of a large number of Australian time series, that the nature of seasonality differs from one series to another, and this is why model selection is crucial for seasonal adjustment. It is further shown that model-based seasonal adjustment could produce results that are significantly different from those obtained by applying the X-11 procedure. Since the X-11 procedure is not based on an explicit model and in view of its other serious drawbacks, it is concluded that the procedure should be abandoned in favour of model-based seasonal adjustment.
Author: Estela Bee Dagum Publisher: Springer ISBN: 3319318225 Category : Business & Economics Languages : en Pages : 293
Book Description
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.
Author: Dominique Ladiray Publisher: Springer Science & Business Media ISBN: 1461301750 Category : Computers Languages : en Pages : 245
Book Description
The most widely used statistical method in seasonal adjustment is implemented in the X-11 Variant of the Census Method II Seasonal Adjustment Program. Developed by the US Bureau of the Census, it resulted in the X-11-ARIMA software and the X-12-ARIMA. While these integrate parametric methods, they remain close to the initial X-11 method, and it is this "core" that Seasonal Adjustment with the X-11 Method focuses on. It will be an important reference for government agencies, and other serious users of economic data.
Author: Estela Bee Dagum Publisher: ISBN: Category : Seasonal variations (Economics) Languages : en Pages : 22
Book Description
"Two strategies have been followed for the development of model-based seasonal adjustment methods. One, where each of the unobserved components, trend-cycle, seasonal and irregular is assumed to follow a normal stochastic process of the ARIMA class and the other, where the observed data are assumed to follow an ARIMA process and from it similar kind of models are derived for the components. The first approach is known as "structural" and the second, as the "reduced-form" given their similarities to the problems of identification of structures from the data in econometrics. This paper discusses the major properties and operational limitations of these two approaches. It also analyses the salient characteristics of the empirical comparisons made between model-based seasonal adjustment methods and the X-11-ARIMA which is used by the majority of government statistical agencies"--Abstract.
Author: F. A. G. den Butter Publisher: North Holland ISBN: Category : Seasonal variations (Economics) Languages : en Pages : 236
Book Description
Presented in this book is the theory and the practice of seasonal adjustment of economic series from the viewpoint of economic policy design. The book offers the economist and practical statistician the opportunity to acquire new and important analytical insights as well as practical tools. Moreover, it discusses the historical development of the practice of seasonal adjustment as applied for policy analysis with Persons in the early twenties, via Zaycoff and Mendershausen in the thirties, through present day modelling with the aid of Kalman filters. Each method treated is empirically illustrated while a comparative analysis is made to assess the appropriateness of the various methods.
Author: Guy Huot Publisher: ISBN: Category : Box-Jenkins forecasting Languages : en Pages : 27
Book Description
"Concurrent seasonally adjusted values are subject to revision when more data become available. This study attempts to analyse the total revision associated with the concurrent seasonal filter for the X-11-ARIMA seasonal adjustment method. The total revision is defined as the mean-squared difference between the frequency response functions of the central and concurrent filters at certain frequencies. Four ARIMA models are considered which are used in the construction of the filter weights. We determine total revision for different forecast horizons and different ARIMA parameter values. Then we evaluate for different forecast horizons the sensitivity of total revision to change in model parameter values"--Abstract.
Author: Imad A. Moosa
Author: Imad A. Moosa
Author: Estela Bee Dagum
Author: Dominique Ladiray
Author: Jörg Breitung
Author: S.C. HILLMER
Author: David A. Pierce
Author: Estela Bee Dagum
Author: S. C. Hillmer
Author: F. A. G. den Butter
Author: Guy Huot