Testing the value of lead information in forecasting monthly changes in employment from the Bureau of Labor Statistics
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DOI: 10.1080/09603107.2014.887190
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Citations
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Cited by:
- 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,"
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National Bureau of Economic Research, Inc.
- Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher J. Kurz, 2019. "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," Finance and Economics Discussion Series 2019-065, Board of Governors of the Federal Reserve System (U.S.).
- 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 Working Papers 26033, National Bureau of Economic Research, Inc.
- 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.
- 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.
- 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.).
- 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.
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