Journal of Business Cycle Research

, Volume 15, Issue 1, pp 73–95 | Cite as

CAMPLET: Seasonal Adjustment Without Revisions

  • Barend Abeln
  • Jan P. A. M. JacobsEmail author
  • Pim Ouwehand


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.


Seasonal adjustment Simulations Employment Operational income Real GDP 

JEL Classification

C22 E24 E32 E37 



We would like to thank William R. Bell, Dean Croushore, Jan De Gooijer, Yvan Lengwiler, Simon van Norden and Jan-Egbert Sturm for helpful suggestions; Tucker McElroy, Dominique Ladiray and Gianluigi Mazzi for stimulating discussions; and participants at the Conference in Honour of Denise R. Osborn, Manchester, in particular Farshid Vahid, a seminar at KOF Zurich, the International Seminar of Forecasting, Rotterdam, the Netherlands, the 8th International Conference on Computational and Financial Econometrics (CFE 2014), University of Pisa, Italy, the 13th Conjunctuurdag, The Hague, the Netherlands, the CIRET/KOF/RIED WSE conference Economic Cycles and Uncertainty, Warsaw, Warsaw, Poland, the 9th International Conference on Computational and Financial Econometrics (CFE 2015), London, the Joint Statistical Meetings, Chicago Ill, the 10th International Conference on Computational and Financial Econometrics (CFE 2016), Seville and the ESMD Seasonal Workshop, US Census Bureau, Washington DC. Helpful comments and suggestions were also received from this journal’s editor, Michael Graff, and three anonymous referees.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.BussumThe Netherlands
  2. 2.Faculty of Economics and BusinessUniversity of GroningenGroningenThe Netherlands
  3. 3.Statistics Netherlands (CBS)The HagueThe Netherlands

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