Seasonal Adjustment

Abstract

Abstract In this chapter some aspects of seasonally adjusting economic time series are discussed. The basic assumption underlying seasonal adjustment methods is that seasonality is some form of data contamination that can be removed from the data in order to facilitate the analysis of, for example, trends and business cycles, and hence that a seasonal time series can be decomposed into various separate unobserved components which can be estimated from the data. The assumption that seasonality may be removed from a time series has led to three approaches, two of which will briefly be discussed in Section 4.1. The first and simplest approach amounts to a regression of the time series on functions of time. The second approach is that the seasonal fluctuations can be filtered out using a sequence of moving average filters. This approach is followed in the regularly applied Census X-11 method. The third and so-called model-dependent approach considers the construction of ARIMA type time series models for the various unobserved components including the seasonal component. Such an ARIMA model can then be used to indicate how to adjust the time series seasonally. In Section 4.1 these last two approaches are discussed.