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.
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1 | (6) |
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1.1 Seasonality and Seasonal Adjustment |
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3 | (1) |
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2 CAMPLET: Seasonal Adjustment Without Revisions |
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2.2.1 Seasonals and Non-seasonals |
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2.2.2 Seasonal Adjustment in CAMPLET |
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2.2.4 Outliers and Change in Seasonal Pattern |
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2.2.5 Automatic Parameter Adjustment for Volatile Series |
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2.3.4 Current Vintage Comparison |
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2.3.5 Quasi-Real-Time Comparison Experiment |
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2.4.1 U.S. Non-farm Payroll Employment |
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2.4.3 Real GDP in the Netherlands |
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3 Seasonal Adjustment of Economic Tendency Survey Data |
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3.2 Seasonal Adjustment: Census and CAMPLET |
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3.4 Empirical Illustration |
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3.4.1 Comparison of KOF-Census and CAMPLET Seasonally Adjusted Variables |
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3.4.2 Implications for the KOF Barometer |
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4 Residual Seasonality: A Comparison of X13 and CAMPLET |
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4.2 Measuring Seasonality |
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4.4 Seasonality and Residual Seasonality Outcomes |
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5 COVID-19 and Seasonal Adjustment |
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5.2 Seasonal Adjustment Methodology |
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5.2.1 Seasonal Decomposition |
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5.2.2 Description of Methods Used in This
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5.2.3 Adjustments Because of the COVID-19 Crisis |
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5.3.1 Date and Settings of Seasonal Adjustment Methods |
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6 Seasonal Adjustment of Daily Data with CAMPLET |
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6.2 Seasonal Adjustment of Daily Date |
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6.3 Adjustments in CAMPLET for Daily Date |
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6.5 Gas Consumption and Production in the Netherlands |
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6.5.2 Gas Consumption and Production, and Mean Temperature |
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| Bibliography |
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Barend Abeln began his career at Unilever (Rotterdam, Netherlands) in the economics department, where he reported on macroeconomic developments in European countries. Subsequently, he became a product manager at Unilever Frozen Foods (Utrecht). In 1972, he entered the real estate market as a founder and CEO of Vlakland Planontwikkeling BV, realizing five second-home projects and a hotel on the French Côte d'Azur. In 1982, he became a private investment consultant in Amsterdam and developed a state-of-the-art seasonal adjustment process.
Jan P.A.M. Jacobs studied econometrics at the University of Groningen (Netherlands) and played volleyball at international level (46 international matches). After a brief position at Philips Medical Systems, he returned to his alma mater and received his Ph.D. on "Econometric Business Cycle Research" in 1998. He is an associate professor at the Faculty of Economics of the University of Groningen, where he taught applied macroecometrics to research master students and supervises bachelor, master graduate theses.
He published more than forty articles in peer-reviewed journals in economics (Journal of Econometrics, Journal of Applied Econometrics, Journal of Business & Economic Statistics, Macroeconomics Dynamics, Scandinavian Journal of Economics, Journal of Macroeconomics), law (International Journal of Law in the Built Environment), and medicine (BMC Family Practice, Economics and Human Biology).
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