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Testing the value of lead information in forecasting monthly changes in employment from the Bureau of Labor Statistics

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  • Allan W. Gregory
  • Hui Zhu

Abstract

This article examines the value of lead information by investigating the predictive power the automatic data processing (ADP) report has on nonfarm payroll employment data released by the Bureau of Labor Statistics (BLS) 2 days after the ADP. We find that updating a vector autoregression (VAR) forecast with the ADP data improves the forecast accuracy relative to a standard VAR forecast. However, this informational advantage disappears if real-time comparisons are made with the Bloomberg consensus forecasts of the BLS which are available prior to the ADP. We explore the confounding effects of data revisions and the potential pitfalls in testing the value of lead information based on the accumulated historical data.

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  • Allan W. Gregory & Hui Zhu, 2014. "Testing the value of lead information in forecasting monthly changes in employment from the Bureau of Labor Statistics," Applied Financial Economics, Taylor & Francis Journals, vol. 24(7), pages 505-514, April.
    • Handle: RePEc:taf:apfiec:v:24:y:2014:i:7:p:505-514
    DOI: 10.1080/09603107.2014.887190
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    Cited by:

    1. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher J. Kurz & Tyler Radler, 2018. "Using Payroll Processor Microdata to Measure Aggregate Labor Market Activity," Finance and Economics Discussion Series 2018-005, Board of Governors of the Federal Reserve System (U.S.).
    2. Klein, Tony, 2022. "Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shock," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 264-286.
    3. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher Kurz, 2019. "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 147-170, National Bureau of Economic Research, Inc.
    4. Levent Bulut, 2017. "Does Statistical Significance Help to Evaluate Predictive Performance of Competing Models?," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 1(1), pages 1-13.
    5. Klein, Tony, 2021. "Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock," QBS Working Paper Series 2021/07, Queen's University Belfast, Queen's Business School.

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