Seasonal Adjustment for Weekly Unemployment Insurance Claims
The Department of Labor's Employment and Training Administration (ETA) contracts with the Bureau of Labor Statistics (BLS) to run the annual programs for weekly unemployment insurance (UI) claims seasonal adjustment.
ETA collects the weekly UI claims reported by each state's unemployment insurance program offices and publishes a weekly news release. ETA uses the set of seasonal factors BLS provides annually and applies them to the unadjusted data from the regular UI program during that year. Concurrent with the implementation and release of the new seasonal factors, ETA incorporates revisions to the UI claims historical series caused by updates to the unadjusted data. ETA is the technical expert regarding UI claims data, and maintains these data at https://oui.doleta.gov/unemploy/claims.asp.
Once a year, BLS updates the models used to calculate seasonal adjustment factors for weekly UI claims. This process includes using all of the regular UI claims data available at that time, updating the parameter files, and identifying potential outliers. BLS is the technical expert regarding the seasonal adjustment process.
What is Seasonal Adjustment?
Seasonal adjustment is a statistical technique that attempts to measure and remove the influences of predictable seasonal patterns to reveal how weekly UI initial and continuing claims change from week to week.
Over the course of a year, the amount of UI claims undergoes fluctuations due to seasonal events including changes in weather, 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 data from week to week. These seasonal adjustments make it easier to observe the cyclical, underlying trend, and other nonseasonal movements in the series.
Note that ETA incorporated additive seasonal factors with the UI claims data issued through the news release on September 3, 2020.
ETA Weekly UI Claims News Releases
History of UI Claims Seasonal Adjustment
Prior to 2002, a method of seasonal adjustment that was developed by staff of the Federal Reserve Bank (FRB) and BLS had been used for at least two decades. Data users voiced concerns about the volatility in the weekly seasonally adjusted initial claims estimates during highly seasonal periods. BLS evaluated an alternate method developed by the FRB and found it to improve the weekly seasonal adjustment over such periods. BLS and ETA introduced this alternate method to develop seasonal factors on April 11, 2002, effective with the release of claims data for the week ending April 6, 2002.
The pre-2002 method of seasonal adjustment assumed that the claims series had a fixed seasonality. That is, the claims data reflect a holiday or regular seasonal event the same way each year and the seasonal factors change only from the effects of the calendar. The alternate method assumes that the claims series exhibit variation in response to a seasonal event (moving seasonality). The alternate method allows the coefficients that determine the factors to change over time, in addition to reflecting the change based on calendar effects. (As part of testing the alternate method, it was confirmed that the claims series does in fact exhibit moving seasonality.)
In the development of seasonal factors, the alternate method uses claims data from the first few weeks of January. Extending the period into the current year more fully accounts for claimant activity during the holiday period and better captures seasonal movement. The pre-2002 method incorporated data only through December of the prior year in the development of new seasonal factors.
Coronavirus (COVID-19) pandemic impact
When BLS projects seasonal adjustment factors, we only use historical data in the models. That means we calculate factors in advance, so they are not influenced by the most recent trends. But the coronavirus pandemic impacted seasonal adjustment in other ways during 2020.
One of the steps during the seasonal adjustment process is to select a modeleither additive or multiplicative. We use an additive model when seasonal movements are stable over time regardless of the level of the series. A multiplicative model is better to use when seasonal movements become larger as the series itself increasesthat is, the seasonality is proportional to the level of the series. That means a sudden large change in the level of a series, such as the large increase in the number of people filing for UI in late March and April 2020, will be accompanied by a proportionally large seasonal effect. When there are large shifts in a measure, multiplicative seasonal adjustment factors can result in adjusting too much or too little. In these cases, additive seasonal adjustment factors usually reflect seasonal movements more accurately and have smaller revisions.
Prior to September 2020, the seasonally adjusted UI claims series used multiplicative seasonal adjustment factors. BLS recommended switching the model of these series to additive. ETA incorporated additive seasonal factors with the UI claims data issued through the news release on September 3, 2020.
When new factors for 2021 and revised historical factors were calculated, BLS examined the effects of the pandemic on the seasonal adjustment models. As a result, many observations in 2020 were identified as outliers.
For Additional Information
Please submit inquiries to the Local Area Unemployment Statistics program.
Last Modified Date: March 24, 2021