Predicting Benchmarked US State Employment Data in Real Time

Scott Brave, Charles Gascon, William Kluender and Thomas Walstrum

No 2019-037, Working Papers from Federal Reserve Bank of St. Louis

Abstract: US payroll employment data come from a survey and are subject to revisions. While revisions are generally small at the national level, they can be large enough at the state level to alter assessments of current economic conditions. Users must therefore exercise caution in interpreting state employment data until they are “benchmarked” against administrative data 5–16 months after the reference period. This paper develops a state-space model that predicts benchmarked state employment data in real time. The model has two distinct features: 1) an explicit model of the data revision process and 2) a dynamic factor model that incorporates real-time information from other state-level labor market indicators. We find that the model reduces the average size of benchmark revisions by about 11 percent. When we optimally average the model’s predictions with those of existing models, the model reduces the average size of the revisions by about 14 percent.

Keywords: Benchmarking methods; Real-time data; Revisions; Forecasting accuracy; time series; Nowcasting; US employment (search for similar items in EconPapers)
JEL-codes: C53 R11 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2019-11-29, Revised 2021-03-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in International Journal of Forecasting

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Related works:
Journal Article: Predicting benchmarked US state employment data in real time (2021)
Working Paper: Predicting Benchmarked US State Employment Data in Realtime (2019)
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DOI: 10.20955/wp.2019.037

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