If you have more questions, please see the Seasonal Adjustment FAQ.
Anything not included in the trend-cycle or the seasonal effects (or in estimated trading day or holiday effects). Its values are unpredictable when it comes to timing, impact, and duration. Irregular can arise from sampling error, non-sampling error, unseasonable weather, natural disasters, strikes, etc.
Effects from holidays that are not always on the same day of a month, such as Labor Day or Thanksgiving in the United States. The most important moving holiday in the US is Easter, and it not only moves between days, but can also move between months since it can occur in March or April.
The process of estimating and removing the seasonal effects from a time series. The basic goal of seasonal adjustment is to decompose a time series into a several different components including a seasonal component and an irregular component.
Effects that are reasonably stable in terms of annual timing, direction, and magnitude. Possible causes include natural factors (the weather), administrative measures (starting and ending dates of the school year), and social/cultural/religious traditions (fixed holidays such as Christmas). Effects associated with the dates of moving holidays like Easter are not seasonal in this sense, because they occur in different calendar months depending on the date of the holiday.
A sequence of measures of a given phenomenon taken at regular time intervals. For the information to be useful for time series analysis, that data should be comparable over time. That means 1) that the measurements should be taken over discrete consecutive periods, i.e., every month or every quarter, and 2) that the definition of the concept and the way it is measured should be consistent over time.
Recurring effects associated with individual days of the week. This occurs because only non-leap-year Februaries have four of each day - four Mondays, four Tuesdays, etc. All other months have an excess of some types of days. If an activity in higher on some days compared to others, then the series can have a trading day effect. For example, building permit offices are usually closed on Saturday and Sunday. Thus, the number of building permits issued in a given month is likely to be higher if the month contains a surplus of weekdays and lower if the month contains a surplus of weekend days.
An estimate of the local level of the series for each month (quarter) derived from the surrounding recent (a year or two) observations.
The seasonal adjustment software used at the U.S. Census Bureau. This program is written and maintained by staff at the Bureau. The primary programmer is Brian Monsell in the Statistical Research Division at the US Census Bureau.
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Last modified: 26 February 2010