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Do Government Agencies Use Public Data?: The Case of GNP

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  • Rathjens, Peter
  • Robins, Russell P

Abstract

In 1991, the U.S. Council of Economic Advisers undertook an initiative to increase the quality of economic statistics. One specific objective was to reduce the size of revisions in GNP estimates. The authors present evidence that one straightforward and inexpensive way of forwarding this objective is for the Department of Commerce to utilize better publicly available information released by other governmental agencies. An important caveat, however, applies: the relationship appears to be nonlinear. Specifically, the inefficient use of information is concentrated in those quarters where the change in the preliminary GNP estimate is large in absolute value. Copyright 1995 by MIT Press.

Suggested Citation

  • Rathjens, Peter & Robins, Russell P, 1995. "Do Government Agencies Use Public Data?: The Case of GNP," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 170-172, February.
    • Handle: RePEc:tpr:restat:v:77:y:1995:i:1:p:170-72
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    Cited by:

    1. Andres Fernandez & Norman R. Swanson, 2009. "Real-time datasets really do make a difference: definitional change, data release, and forecasting," Working Papers 09-28, Federal Reserve Bank of Philadelphia.
    2. van Dijk, D.J.C. & Franses, Ph.H.B.F. & Ravazzolo, F., 2007. "Evaluating real-time forecasts in real-time," Econometric Institute Research Papers EI 2007-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. M. Mogliani & T. Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    4. Jordi Pons, 1999. "Evaluating the OECD's forecasts for economic growth," Applied Economics, Taylor & Francis Journals, vol. 31(7), pages 893-902.
    5. Corradi, Valentina & Fernandez, Andres & Swanson, Norman R., 2009. "Information in the Revision Process of Real-Time Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 455-467.

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