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Office of Survey Methods Research
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Published Year: 2026
Survey/Program(s): Local Area Unemployment Statistics (LAUS), Current Population Survey (CPS),
Catalog: Statistical Survey Paper
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Modeling CPS Labor Force Estimates with Missing Observations Due to the Federal Government Shutdown in 2025
Abstract
BLS produces monthly local area unemployment estimates for states based on data from the Current Population Survey (CPS). Due to small sample sizes, structural time series models expressed in state-space form, requiring a continuous time series, are fit to CPS estimates to produce more stable estimates. A federal government shutdown interrupted data collection resulting in missing data for October 2025. Fortunately, the Kalman filter (KF) algorithm used to generate the model estimates readily accommodates missing observations. A well-known solution to missing data is to skip the update step in the KF algorithm. As new data accumulates, a smoothing process is used to revise the predictions.
This paper shows the advantage of using the KF algorithm prediction step to impute missing values. The initial predicted values along with their standard errors for October are compared with previous values with complete data to quantify the effects of uncertainty due to the missing data. Also shown are the effects of revisions to the initial predictions for October as additional data become available. The October estimates used in the paper are unofficial and unpublished.