Seasonal adjustment is a statistical technique that attempts to measure and remove the influences of predictable
seasonal patterns to reveal how employment and unemployment change from month to month.
Over the course of a year, the size of the labor force and levels of employment and unemployment undergo fluctuations
due to seasonal events including changes in weather, harvests, major holidays, and school schedules. Because these seasonal
events follow a more or less regular pattern each year, their influence on statistical trends can be eliminated by seasonally
adjusting the statistics from month to month. These seasonal adjustments make it easier to observe the cyclical, underlying
trend, and other non-seasonal movements in the series.
The LAUS program uses times-series models to produce employment and unemployment estimates for the states and the District
of Columbia, census regions and divisions, and a handful of large substate areas and their respective balance-of-state areas.
These models are benchmarked, or forced to sum, to the national not-seasonally-adjusted estimates of employment and unemployment
from the Current Population Survey on a monthly basis. The benchmarked data first are adjusted using an X-11 type of seasonal
adjustment filter. The adjusted data then are smoothed using a Reproducing Kernel Hilbert Space (RKHS) filter. The resulting
smoothed seasonally adjusted estimates are analyzed in the monthly State Employment and
Unemployment news release and published in the BLS time-series database.
Employment and unemployment estimates for non-modeled metropolitan areas and metropolitan divisions are also seasonally adjusted each
month, using a technique known as SEATS, or Signal Extraction in ARIMA (Auto Regressive Integrated Moving Average) Time Series. Although
these data are not analyzed in the Metropolitan Area Employment and Unemployment news release or loaded into the BLS time-series
database, they are available via the supplemental tables at www.bls.gov/lau/metrossa.htm.
Last Modified Date: February 27, 2018