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29.2.        Need for seasonally adjusted data. Monthly and quarterly data on international merchandise trade statistics are an important tool for economic policymaking, business cycle analysis, modelling and forecasting. However, they are often characterized by seasonal fluctuations and other calendar or trading-day effects, which mask other characteristics of the data which are of interest to analysts. Seasonal adjustment is a process of estimating and removing seasonal or calendar influences from a time series in order to achieve a better knowledge of the underlying behaviour. 

29.3.        Seasonal adjustment method. Because national circumstances vary from one country to another, no preferred seasonal adjustment method is recommended. If seasonally adjusted data are published, it is recommended that information on the adjustment methods be provided by countries in their metadata (IMTS 2010, para. 11.4). 

29.4.        Concept of seasonal adjustment. Seasonal adjustment is the process of estimating and removing effects in a sub-annual time series that occur at about the same time and magnitude each year, as well as calendar-related systematic effects that are not stable in annual timing, which are often large enough to mask other data characteristics. Removing the seasonal component allows for an easier comparison of long- and short-term movements across sectors and countries and further contributes to an understanding of the non-seasonal behaviour which is often of interest for economic policymaking, business cycle analysis, modelling and forecasting. 

29.5.        Components of time series. A time series is generally considered to consist of trend, cycle, seasonal and irregular components. The trend, cycle and irregular components together reflect long-term movements lasting many years, fluctuations relating to the business cycle, and unforeseeable movements of all kinds. The seasonal component of a time series represents the movement within the year, and includes the effect of climatic and institutional events that are repeated regularly throughout the year, as well as calendar-related systematic effects that are not stable in annual timing, such as trading-day and moving holiday effects. Seasonal adjustment is the process of completely eliminating the seasonal component from the original time series. 

29.6.        Tools used for seasonal adjustment. Seasonal adjustment is typically accomplished with the assistance of free and publicly available software packages, the most widespread of which are TRAMO-SEATS (supported by the Bank of Spain) and X-12-ARIMA (supported by the United States Census Bureau).[1] As the seasonal component is not precisely defined, seasonal adjustment often depends on the a priori hypotheses underlying the model chosen and upon the software and specifications chosen.



[1] X-12-ARIMA is based on moving averages and includes a time-series modelling component, the ability to produce multiplicative as well as additive seasonal adjustment, and systematic removal of calendar effects. In July 2012, the United States Census Bureau  released X-13ARIMA-SEATS which it developed in collaboration with the Bank of Spain (see http://www.census.gov/srd/www/x13as/), which integrates an enhanced version of X-12-ARIMA with an enhanced version of SEATS.