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 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.

Multiresolution Forecasting for Industrial Applications

“…The current state-of-the-art method, according to accessible open-sources for time series forecasting in Python and R, contain the following techniques: ARIMA, Cubic Spline extrapolation, decomposition models, exponential smoothing, Croston, MAPA, naive/random walks, neural networks, Prophet and the theta method. Two automatized forecasting methods are used to represent the current state of the art for ARIMA models: the first one is RJDemetra, which is an ARIMA model with seasonal adjustment, according to the "ESS Guidelines on Seasonal Adjustment" [46] available from the National Bank of Belgium, using two leading concepts TRAMO-SEATS+ and X-12ARIMA/X-13ARIMA-SEATS [3] and referred to as "SARIMA" (or short "SA") [47] and an automatized ARIMA referred to as "AutoARIMA" (or short "AA") [48,49]. Modeling ARIMA for time series forecasting follows an objective and thus can be completely automatized by optimizing an information criterion for which AutoARIMA and SARIMA are two different approaches [48].…”

“…Seasonal time series forecasting with computers had early success with seasonal adjusted methods as proposed in 1978 [1] or with the the X-11 method [2]. Over the years, improved versions, such as the X-13 [3], various variants of such models and new techniques in the area of statistical models, were developed [4], and a new field emerged in computational intelligence dealing with seasonal time series forecasting arose [5]. Recently, a method based on Fourier analysis was introduced [6].…”

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.).…”

COVID-19 and Seasonal Adjustment

“…The program consists of a simpleadaptive 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.For details seeAbeln et al (2019). The package can be download at http://www.…”