What is seasonal adjustment?
Seasonal adjustment is a statistical technique which eliminates the influences of weather, holidays, the opening and closing of schools, and other recurring seasonal events from economic time series. This permits easier analysis of cyclical, trend, and other nonseasonal movements in the data. By eliminating seasonal fluctuations, the series becomes smoother and it is easier to compare data from month to month.
Over the course of a year, the levels of employment and the associated job flows undergo sharp fluctuations due to such seasonal events as changes in the weather, reduced or expanded production, harvests, major holidays, and the opening and closing of schools. The effect of such seasonal variation can be very large. Because these seasonal events follow a more or less regular pattern each year, adjusting these statistics from quarter to quarter can eliminate their influence. These adjustments make non-seasonal developments, such as declines in economic activity, easier to spot. The adjusted figure provides a more useful tool with which to analyze changes in economic activity.
Seasonal adjustment is the process of estimating and removing seasonal effects from a time series in order to better reveal certain non-seasonal features. Examples of seasonal effects include a July drop in automobile production as factories retool for new models and increases in heating oil production during September in anticipation of the winter heating season.(Seasonal effects are defined more precisely in 5. and 6. below.) Sometimes we also estimate and remove trading day effects and moving holiday effects (see 7. below) during the seasonal adjustment process.
Seasonal adjustment is a statistical technique that eliminates the influences of weather, holidays, the opening and closing of schools, and other recurring seasonal events from economic time series. This permits easier observation and analysis of cyclical, trend, and other nonseasonal movements in the data. By eliminating seasonal fluctuations, the series becomes smoother and it is easier to compare data from month to month. In the LAUS program, data for census regions, census divisions, states, the District of Columbia, Puerto Rico, New York city, and the 8 substate areas listed in Question 2 above are seasonally adjusted. For a more complete description of seasonal adjustment and the methodology used to estimate seasonal adjustment factors, see Seasonal Adjustment.
Seasonal adjustment is a statistical technique that attempts to remove data fluctuations attributable to predictable changes that normally occur at the same time and in about the same magnitude every year such as weather, holidays, school openings and closings, and production and business cycles. As a result, seasonally adjusted data are usually preferred for analyzing non-seasonal, month-to-month, and general trends in the economy apart from normal seasonal changes. Unadjusted data are of primary interest to those concerned about the actual estimates of economic indicators reflecting what is happening right now. Unadjusted data also are used extensively for escalation purposes.
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