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Predicting Benchmarked US State Employment Data in Realtime

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  • Scott Brave
  • Charles S. Gascon
  • William Kluender
  • Thomas Walstrum

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.

Suggested Citation

  • Scott Brave & Charles S. Gascon & William Kluender & Thomas Walstrum, 2019. "Predicting Benchmarked US State Employment Data in Realtime," Working Paper Series WP 2019-11, Federal Reserve Bank of Chicago.
  • Handle: RePEc:fip:fedhwp:87482
    DOI: 10.21033/wp-2019-11
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    Cited by:

    1. Daniel Aaronson & Scott A. Brave & Michael Fogarty & Ezra Karger & Spencer D. Krane, 2021. "Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade," Working Paper Series WP-2021-05, Federal Reserve Bank of Chicago, revised 18 Jun 2021.

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    More about this item

    Keywords

    dynamic factor models; Employment; Data revisions; nowcasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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