Concurrent Seasonal Adjustment for CES State and Area Program
With the release of January 2018 data on March 12, 2018, the Current Employment Statistics (CES)
State and Area program will convert to concurrent seasonal adjustment, which uses all available
estimates, including those for the current month, in developing seasonal factors. Currently, the
CES program projects seasonal factors once a year during the annual benchmark process.
CES State and Area current annual seasonal adjustment process requires 10 years of historical
sample data as the input to the X-13 ARIMA model to create forecasted factors that are used to
seasonally adjust sample estimates for the reminder of the year. The concurrent seasonal adjustment
process will use the same historical sample data. Therefore, the ARIMA model, outliers, and calendar
effects determined during the annual review process are used in concurrent seasonal adjustment. The
only difference in inputs between the two methods of seasonal adjustment is the incorporation of
real-time estimates with concurrent seasonal adjustment.
This transition will align CES State and Area methodology with a broad range of statistical programs
that have recognized the superiority of concurrent seasonal adjustment. Empirical research starting
in the 1980s has generally concluded that concurrent seasonal adjustment is the recommended
methodology.1 The CES National program incorporated concurrent seasonal
adjustment in 2003.2 In addition, numerous BLS data series including
The Business Employment Dynamics, Job Openings and Labor Turnover Survey, and Local Area Unemployment
Statistics are utilizing concurrent seasonal adjustment.
CES State and Area research confirms the viability of concurrent seasonal adjustment for over three
years. The results show that concurrent seasonal adjustment will reduce the revisions of the seasonally
adjusted estimates compared to seasonally adjusted benchmark data as well as reduce the month-to-month
variability of the seasonally adjusted time series.3 These results are
consistent with prior research.
Concurrent seasonal factors are created every month for the current month’s preliminary estimates as
well as the previous month’s final estimates. This is a change from the annual forecast method in
which seasonal factors are produced for the remaining months of the year during the annual seasonal
adjustment process. To assist in the understanding of this new production process, refer to Figure 1 below.
||Benchmarked Historical Values
||Universe factors derived from benchmarked history and applied to benchmarked historical values.
||Concurrent sample factors generated with Jan. preliminary estimates and applied to Oct.,
Nov., Dec. re-estimates and Jan. preliminary estimates.
||Concurrent factors derived each preliminary cycle from all relevant sample history, up to and
including the current month preliminary estimates, and applied to the previous month final
estimates and the current month preliminary estimates.
The annual production of calculating and applying seasonal factors for the benchmark period will remain
unchanged. Concurrent seasonal adjustment factors will first be applied to the re-estimation period in
the fourth quarter of the calendar year. Once the January preliminary estimation cycle is complete,
concurrent factors will be developed for January preliminary estimates as well as for the October,
November, and December re-estimation data. The next instance of concurrent seasonal adjustment will then
occur during the February preliminary cycle. During this time, seasonal factors will simultaneously be
developed for February preliminary estimates and January final estimates. This pattern will repeat every
month until the end of the estimation year.
For additional information regarding concurrent seasonal adjustment, please see the references below.
McKenzie, Sandra K. (1984), “Concurrent Seasonal Adjustment with Census X-11,” Journal of Business and
Economic Statistics 2, 235-249. Available
Pierce, David A., and Sandra K. McKenzie (1987), “On Concurrent Seasonal Adjustment,” Journal of the
American Statistical Association 82, 720-732. Available
Last Modified Date: December 22, 2017