Introduction

Seasonality, which Hylleberg (1986, p. 23) defines as ‘the systematic, although not necessarily regular or unchanging, intrayear movement that is caused by climatic changes, timing of religious festivals, business practices, and expectations’, is often considered a nuisance in economic modeling. 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 this book, we present a novel, alternative 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. We will show among other things that differences between X13ARIMA-SEATS, the industry standard, and CAMPLET are fairly small. Therefore, the disadvantages associated with having to update seasonal adjustments and seasonal factors when new observations become available might be larger than the benefits of using optimal seasonal adjustments.