Predicting Benchmarked Us State Employment Data in Real Time

43 Pages Posted: 14 May 2021

See all articles by Scott A. Brave

Scott A. Brave

Federal Reserve Bank of Chicago

Charles S. Gascon

Federal Reserve Bank of St. Louis

William Kluender

Compass Lexecon

Thomas Walstrum

Federal Reserve Bank of Chicago

Date Written: November, 2019

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.

JEL Classification: C53, R11

Suggested Citation

Brave, Scott A. and Gascon, Charles S. and Kluender, William and Walstrum, Thomas, Predicting Benchmarked Us State Employment Data in Real Time (November, 2019). Available at SSRN: https://ssrn.com/abstract=3843867 or http://dx.doi.org/10.20955/wp.2019.037

Scott A. Brave (Contact Author)

Federal Reserve Bank of Chicago ( email )

230 South LaSalle Street
Chicago, IL 60604
United States

Charles S. Gascon

Federal Reserve Bank of St. Louis ( email )

411 Locust St
Saint Louis, MO 63011
United States

William Kluender

Compass Lexecon

United States

Thomas Walstrum

Federal Reserve Bank of Chicago ( email )

230 South LaSalle Street
Chicago, IL 60604
United States

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