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Predicting Benchmarked US State Employment Data in Real Time

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

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.

Suggested Citation

  • Scott A. Brave & Charles S. Gascon & William Kluender & Thomas Walstrum, 2019. "Predicting Benchmarked US State Employment Data in Real Time," Working Papers 2019-037, Federal Reserve Bank of St. Louis, revised 11 Mar 2021.
  • Handle: RePEc:fip:fedlwp:86649
    DOI: 10.20955/wp.2019.037
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    References listed on IDEAS

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    More about this item

    Keywords

    Benchmarking methods; Real-time data; Revisions; Forecasting accuracy; Time series; Nowcasting; US employment;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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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