Time aggregation in FRED | FRED Blog Skip to main content Explore Our Apps FRED Tools and resources to find and use economic data worldwide FRASER U.S. financial, economic, and banking history ALFRED Vintages of economic data from specific dates in history CASSIDI View banking market concentrations and perform HHI analysis Release Calendar Tools FRED Add-in for Excel FRED API FRED Mobile Apps News Blog About What is FRED Tutorials Digital Badges Contact Us My Account Explore Our Apps Explore Our Apps FRED Tools and resources to find and use economic data worldwide FRASER U.S. financial, economic, and banking history ALFRED Vintages of economic data from specific dates in history CASSIDI View banking market concentrations and perform HHI analysis STL Fed Home Page Release Calendar Tools FRED Add-in for Excel FRED API FRED Mobile Apps News Blog About What is FRED Tutorials Digital Badges Contact Us Search FRED Blog Search for: Recent Posts US manufacturing employment is down, but each state has its own story Real GDP growth by state: First quarter 2026 Primary deficits: a short history What US assets are held overseas? Durable goods inflation and effective tariffs Recent St. Louis Fed research US manufacturing employment is down, but each state has its own story Real GDP growth by state: First quarter 2026 Primary deficits: a short history What US assets are held overseas? 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In the example here, the unemployment rate is collected monthly, but we often have other labor market data collected annually. The question here is how to aggregate the high-frequency data into a lower-frequency statistic. In FRED, we have three options: average, sum, or end of period. In the graph, we compare annual unemployment data taking either the average over the year or the end-of-period observation. The choice of whether to use seasonal adjustment doesn’t affect the average. By definition, seasonal adjustment implies that December, the last month of the year, does not have a systematically different unemployment rate from any other month. However, averaging or summing will systematically give lower measures of variation than the end-of-period observation. The reason is simple, even without too much formal math: Suppose every month our observation is the annual number plus some monthly “noise” term. Either summing or taking the average, we essentially allow these monthly variations to cancel each other out. Taking an observation from the end of period includes all of the month-specific variation. In the graph, we can see that the red line, which takes annual unemployment as the final month’s observation, is more volatile. In fact, from 1979-2014, its coefficient of variation is 25.56%; the blue line, which takes the average, has a coefficient of variation of 24.86%. How this graph was created: Search for “unemployment” and select the seasonally adjusted civilian unemployment rate. Using the pull-down menu, change “Frequency” to “Annual.” The default “Aggregation Method” is “Average,” and we will keep that. Then, “Add Data Series” and again search for “unemployment.” Add a new series using “unrate,” the same data as last time. Again, change it to an annual frequency. But this time, change the aggregation method to “End of Period.” Suggested by David Wiczer. View on FRED, series used in this post: UNRATE Tagged UNRATE -- Back to Top Filter 0 Subscribe to the FRED newsletter Subscribe Follow us Saint Louis Fed linkedin page Saint Louis Fed facebook page Saint Louis Fed X page Saint Louis Fed YouTube page   Need Help? Questions or Comments FRED Help Legal Privacy Notice & Policy