Working Paper

Predicting Benchmarked US State Employment Data in Real Time

Abstract: US payroll employment data come from a survey of nonfarm business establishments and are therefore subject to revisions. While the revisions are generally small at the national level, they can be large enough at the state level to substantially alter assessments of current economic conditions. Researchers and policymakers must therefore exercise caution in interpreting state employment data until they are “benchmarked” against administrative data on the universe of workers some 5 to 16 months after the reference period. This paper develops and tests a state space model that predicts benchmarked US state employment data in realtime. The model has two distinct features: 1) an explicit model of the data revision process and 2) a dynamic factor model that incorporates realtime information from other state-level labor market indicators. We find that across the 50 US states, the model reduces the average size of benchmark revisions by about 9 percent. When we optimally average the model’s predictions with those of existing models, we find that we can reduce the average size of the revisions by about 15 percent.

Keywords: data revisions; nowcasting; dynamic factor models;

JEL Classification: C53; R11;

https://doi.org/10.20955/wp.2019.037

Status: Published in International Journal of Forecasting

Bibliographic Information

Provider: Federal Reserve Bank of St. Louis

Part of Series: Working Papers

Publication Date: 2019-11-29

Number: 2019-037

Related Works

• Publisher Article: Predicting Benchmarked US State Employment Data in Real Time
• Working Paper Revision: Predicting Benchmarked US State Employment Data in Real Time