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Adapting the Seasonal Adjustment of Local Area Unemployment Statistics to the COVID-19 Pandemic

Richard Tiller, Jennifer Oh, and Lizhi Liu


The Covid-19 pandemic delivered an instantaneous shock to the U.S. labor market in March/April 2020. This crisis presents a challenge to seasonal adjustment of labor force data. In this paper we explore various options for seasonally adjusting series during the pandemic using as examples 421 series from the Bureau of Labor Statistics Local Area Unemployment Statistics program. The basic issue is how to prevent distortions in seasonal factor estimation from outliers generated by the pandemic while efficiently using new information generated during the pandemic period. This task is complicated because at the onset of a pandemic there is little data available to estimate its duration and dynamics. Since a large number of series, must be seasonally adjusted, an automated approach is necessary. We explored several options in terms of the sequence and mix of outlier types allowed in the automated modeling process and used information criteria to select the parsimonious model.