Seasonality in economic time series can "obscure" movements of other components in a series that are operationally more important for economic and econometric analyses. In practice, one often prefers to work with seasonally adjusted data to assess the current state of the economy and its future course. This book presents a seasonal adjustment program called CAMPLET, an acronym of its tuning parameters, which consists of a simple adaptive procedure to extract the seasonal and the non-seasonal component from an observed series. Once this process is carried out, there will be no need to revise these components at a later stage when new observations become available. The authors describe the main features of CAMPLET, evaluate the outcomes of CAMPLET and X-13ARIMA-SEATS in a controlled simulation framework using a variety of data generating processes, and illustrate CAMPLET and X-13ARIMA-SEATS with three time series: US non-farm payroll employment, operational income of Ahold and real GDP in the Netherlands. Furthermore they show how CAMPLET performs under the COVID-19 crisis, and its attractiveness in dealing with daily data. This book appeals to scholars and students of econometrics and statistics, interested in the application of statistical methods for empirical economic modeling.

Seasonality in economic time series can "obscure" movements of other components in a series that are operationally more important for economic and econometric analyses. In practice, one often prefers to work with seasonally adjusted data to assess the current state of the economy and its future course. This book presents a seasonal adjustment program called CAMPLET, an acronym of its tuning parameters, which consists of a simple adaptive procedure to extract the seasonal and the non-seasonal component from an observed series. Once this process is carried out, there will be no need to revise these components at a later stage when new observations become available. The authors describe the main features of CAMPLET, evaluate the outcomes of CAMPLET and X-13ARIMA-SEATS in a controlled simulation framework using a variety of data generating processes, and illustrate CAMPLET and X-13ARIMA-SEATS with three time series: US non-farm payroll employment, operational income of Ahold and real GDP in the Netherlands. Furthermore they show how CAMPLET performs under the COVID-19 crisis, and its attractiveness in dealing with daily data. This book appeals to scholars and students of econometrics and statistics, interested in the application of statistical methods for empirical economic modeling.

Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time s

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.

This report is a reissue of a paper entitled, "Electronic computer seasonal adjustments--tests and revisions of U.S. census methods" [by Julius Shiskin]. The paper was presented at a conference on the use of electronic computers for seasonal adjustments held in Paris, November 28-30, 1960, under the auspices of the Organization for European Economic Cooperation.

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.