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Thompson says America must "unchoose" policies that have led to mass incarceration

Axinn says new data on campus rape will "allow students to see for themselves the full extent of this problem"

Frey says white population is growing in Detroit and other large cities


Susan Murphy to speak at U-M kickoff for data science initiative, Oct 6, Rackham

Andrew Goodman-Bacon, former trainee, wins 2015 Nevins Prize for best dissertation in economic history

Deirdre Bloome wins ASA award for work on racial inequality and intergenerational transmission

Bob Willis awarded 2015 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics

Next Brown Bag

Monday, Oct 12 at noon, 6050 ISR
Joe Grengs: Policy & planning for transportation equity

Imputing for Late Reporting in the U.S. Current Employment Statistics Survey

Archived Abstract of Former PSC Researcher

Copeland, Kennon, and Richard L. Valliant. 2007. "Imputing for Late Reporting in the U.S. Current Employment Statistics Survey." Journal of Official Statistics, 23(1): 69--90.

Surveys of economic conditions are often published monthly to provide up-to-date measures of the state of a country’s economy. In establishment surveys, some sample units may not report in time to be included in the current month’s estimates, but eventually do report data. This late reporting can lead to revisions of estimates as more sample data become available. To maintain credibility, it is important that the size of revisions be kept as small as possible. We study this issue using the U.S. Current Employment Statistics (CES) survey. A model-based view of the CES weighted link relative estimator is used to identify potential bias due to model misspecification. An alternative approach, involving imputation for missing data, is used in an attempt to reduce the magnitude of revisions between preliminary and final estimates of employment for a month. The alternative, while not yielding statistically significant improvement in monthly revisions at the industry level, offers the potential for improved estimates for lower level aggregation.

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