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Linear Empirical Bayes Prediction of Employment Growth Rates Using the U.S. Current Employment Statistics Survey

In: Strategic Management, Decision Theory, and Decision Science

Author

Listed:
  • P. Lahiri

    (University of Maryland)

  • Bogong T. Li

    (U.S. Bureau of Labor Statistics)

Abstract

Many government agencies report monthly estimates of various economic indicators based on business surveys of establishments using traditional design-based methods. Late reporting is a common phenomenon in these surveys and agencies publish revisions of their preliminary estimates as they receive information from late reporters. To maintain public trust, it is important that these revisions are not very different from the preliminary estimates. In the context of the Current Employment Statistics (CES) program of the U.S. Bureau of Labor Statistics, we develop a robust Bayesian two-level model that is useful in reducing the amount of revision of preliminary estimates. The first level of the model develops a relationship between preliminary and final design-based estimates. The second level examines the effects of different labor market factors on final design-based estimates. A linear empirical Bayes prediction (LEBP) method, a robust method based on certain moment assumptions, is proposed to implement the Bayesian model in which the hyperparameters are estimated from the historical data. Using the CES survey data, we show that the LEBP is effective in cutting down the revision substantially and offers a promising alternative to the customary design-based approach.

Suggested Citation

  • P. Lahiri & Bogong T. Li, 2021. "Linear Empirical Bayes Prediction of Employment Growth Rates Using the U.S. Current Employment Statistics Survey," Springer Books, in: Bikas Kumar Sinha & Srijib Bhusan Bagchi (ed.), Strategic Management, Decision Theory, and Decision Science, pages 145-158, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-1368-5_10
    DOI: 10.1007/978-981-16-1368-5_10
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