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Abstract:
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Seasonality in macroeconomic time series can obscure movements of other components in a series that are operationally more important for economic and econometric analyses. Indeed, in practice one often prefers to work with seasonally adjusted data to assess the current state of the economy and its future course. In this paper, we introduce a seasonal adjustment program called CAMPLET, an acronym of its tuning parameters, which consists of a simple adaptive procedure to separate the seasonal component and the non-seasonal component from an observed time series. Once this process has been carried out there will be no need to revise these components at a later stage when more observations become available, in contrast with other seasonal adjustment methods. The paper describes the main features of CAMPLET and compares and contrasts it to X-13ARIMA-SEATS. We evaluate the outcomes of both methods in a controlled simulation framework using a variety of processes. Finally, we apply the CAMPLET and X-13ARIMA-SEATS methods to three time series: U.S. non-farm payroll employment, operational income of Ahold, and real GDP in the Netherlands.
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