Question: Which QSS series will be seasonally adjusted?
Answer: The revenue series for the following NAICS codes will be seasonally adjusted:
512 Software publishers
5112 Motion picture and sound recording industries
54 pt Professional, scientific, and technical services (except landscape architectural services and veterinary services)
5411 Legal services
5412 Accounting, tax preparation, bookkeeping, and payroll services
56 pt Administrative and support and waste management and remediation services (except landscape services)
5613 Employment services
5615 Travel arrangement and reservation services
562 Waste management and remediation services
Note: Seasonally adjusted estimates by class of customer will not be produced.
Question: How do you determine which series to seasonally adjust?
Answer: Due to the shortness of the series, seasonally adjusted estimates will be published only for QSS series with indications of a good quality seasonal adjustment. However, research will continue on the seasonal adjustment of additional series.
Question: Will the seasonally adjusted estimates cover the same time frame as the unadjusted estimates?
Answer: Yes, as part of the Annual Benchmark report, estimates will be provided from the fourth quarter of 2003 through the preliminary estimate for the fourth quarter of the most recent year. The quarterly press release contains seasonally adjusted estimates for the most recent six quarters.
Question: What is seasonal adjustment?
Answer: Seasonal adjustment is the process of estimating and removing seasonal effects from a time series in order to better reveal certain non-seasonal features. An example of a seasonal effect is an increase in accounting services during the tax season. (Seasonal effects are defined more precisely below.)
Question: Why do you seasonally adjust data?
Answer: Seasonal movements are often large enough that they mask other characteristics of the data that are of interest to analysts who track current economic trends. For example, if each quarter has a different seasonal tendency toward high or low values it can be difficult to detect the general direction of a time series' recent quarterly movement (increase, decrease, turning point, no change, consistency with another economic indicator, etc.). Seasonal adjustment produces data in which the values of neighboring quarters are usually easier to compare. Many data users prefer seasonally adjusted data because they want to see those characteristics that seasonal movements tend to mask, especially changes in the direction of the series.
Question: What kinds of seasonal effects are removed during seasonal adjustment?
Answer: Seasonal adjustment procedures for quarterly time series estimate effects that occur in the same calendar quarter with similar magnitude and direction from year to year. Examples of these effects include economic activity tied to the tax season or to the travel season. The seasonal factors are estimates of average effects for each quarter. It is important to note that seasonal factors are based on present and past experience and that future data may show a different pattern of seasonal factors.
Question: How is the seasonal adjustment derived?
Answer: We use X-13ARIMA-SEATS to derive our seasonal adjustment and produce seasonal factors. X-13ARIMA-SEATS is a seasonal adjustment program developed at the U.S. Census Bureau in collaboration with the Bank of Spain that integrates an enhanced version of X-12-ARIMA with an enhanced version of SEATS to provide both X-11 method seasonal adjustments and ARIMA model-based seasonal adjustments and diagnostic.
Question: Which version of X-13 is currently used?
Answer: We currently use version 1.0, build 148 of X-13ARIMA-SEATS.