CAMPLET: Seasonal Adjustment Without Revisions

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 nonseasonal 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 of Daily Data with CAMPLET

“…Calendar effects and outliers are typically adjusted for in a pre-treatment step, with a regression with dummies capturing trading days, holidays and outliers (Ladiray et al 2018;McElroy et al 2018), the so-called regARIMA equation. CAMPLET does not require pre-treatment of a time series to adjust for calendar effects and outliers, as shown in Abeln et al (2019) and in Section 3 below.…”

“…Recently, Abeln et al (2019) presented a new seasonal adjustment method CAMPLET, an acronym of its tuning parameters. The method consists of a simple procedure to extract the seasonal and the non-seasonal component from an observed time series.…”

COVID-19 and Seasonal Adjustment

“…For quarterly and monthly data we apply Census X13ARIMA-SEATS (henceforth X13): the combination of Census X12-ARIMA and TRAMO-Seats which has become the industry standard (Department of Commerce Census Bureau http://www.census. gov/srd/www/x13as/), and a recent competitor CAMPLET (Abeln et al 2019). For weekly data, Stock (2021) recommends to transform series to logs, annual or 52 weeks differences, and manual adjustment for problem weeks (moving holidays etc.).…”