 CAMPLET: Seasonal Adjustment Without RevisionsBarend Abeln () and Jan Jacobs Chapter Chapter 2 in Seasonal Adjustment Without Revisions, 2023, pp 7-29 from Springer Abstract: 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 chapter 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. Date: 2023 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:spbchp:978-3-031-22845-2_2 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-22845-2_2 Access Statistics for this chapter More chapters in SpringerBriefs in Economics from Springer |
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