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CAMPLET: Seasonal Adjustment Without Revisions

Author

Listed:
  • Barend Abeln
  • Jan P. A. M. Jacobs

    (University of Groningen)

  • Pim Ouwehand

    (Statistics Netherlands (CBS))

Abstract

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 paper 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 paper describes the main features of CAMPLET. We 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: U.S. non-farm payroll employment, operational income of Ahold and real GDP in the Netherlands.

Suggested Citation

  • Barend Abeln & Jan P. A. M. Jacobs & Pim Ouwehand, 2019. "CAMPLET: Seasonal Adjustment Without Revisions," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(1), pages 73-95, April.
    • Handle: RePEc:spr:jbuscr:v:15:y:2019:i:1:d:10.1007_s41549-018-0031-3
    DOI: 10.1007/s41549-018-0031-3
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    References listed on IDEAS

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    Cited by:

    1. Barend Abeln & Jan P. A. M. Jacobs, 2023. "COVID-19 and Seasonal Adjustment," SpringerBriefs in Economics, in: Seasonal Adjustment Without Revisions, chapter 0, pages 53-61, Springer.
    2. Barend Abeln & Jan P. A. M. Jacobs, 2023. "Seasonal Adjustment of Daily Data with CAMPLET," SpringerBriefs in Economics, in: Seasonal Adjustment Without Revisions, chapter 0, pages 63-78, Springer.
    3. Fleury, Afonso & Fleury, Maria Tereza Leme & Oliveira, Luis & Leao, Pablo, 2024. "Going digital EMNEs: The role of digital maturity capability," International Business Review, Elsevier, vol. 33(4).
    4. Jing Tian & Jan P.A.M. Jacobs & Denise R. Osborn, 2024. "Multivariate Trend‐Cycle‐Seasonal Decompositions with Correlated Innovations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(5), pages 1260-1289, October.
    5. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    6. Eiji Goto & Jan P.A.M. Jacobs & Tara M. Sinclair & Simon van Norden, 2023. "Employment reconciliation and nowcasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(7), pages 1007-1017, November.

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    More about this item

    Keywords

    Seasonal adjustment; Simulations; Employment; Operational income; Real GDP;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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