Introduction

Seasonal adjustment is a process of using analytical techniques to estimate and remove seasonal and calendar effects, which may conceal and distort the true underlying movement of an economic time series. The seasonally adjusted data series facilitate a better assessment of their recent movements, including the timelier identification of turning points. According to the decomposition theory, every time series comprises four components:

• Trend – growth or decline observed over an extended period of time.
• Cycle – sinusoidal fluctuation around the trend, influenced by economic expansions and contractions.
• Seasonal – intra-year periodic variation that repeats itself every year.
• Irregular – short-term erratic random fluctuations, caused by unanticipated events.

Currently, DOS uses the X-12 ARIMA procedure developed by the US Census Bureau, in carrying out seasonal adjustments. This procedure is widely used among national statistical offices around the world. Non-Seasonally Adjusted (NSA) data reflects the actual economic events that have occurred, while Seasonally Adjusted (SA) data represents an analytical elaboration of the data designed to show the underlying movements that may be hidden by the seasonal variation. SA data is particularly useful during instances where NSA data contains strong seasonal patterns and hinders detailed in-depth data analysis. In this case, NSA data period-on-period (e.g. month-on-month or quarter-on-quarter) growths may be masked by seasonal fluctuations and only year-on-year growths, which are not as sensitive in detecting short-term changes in growth momentum, may be quoted for reporting purposes.