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Federal Budget Projections: A Nonparametric Assessment of Bias and Efficiency

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  • Campbell, Bryan
  • Ghysels, Eric

Abstract

As an important initial step in the annual budget process, the President presents to Congress each January his budget with details of federal spending activity and priorities. Our paper is a statistical assessment of the merit of the budget figures submitted to Congress. We investigate the overall budget as well as several important specific accounts. An important aspect of our paper is the introduction of a nonparametric methodology which incorporates exact tests for assessing the unbiasedness, and the internal and external consistency of forecasts. The empirical evidence shows that the nonparametric results confirm the presence of bias in forecasts on the outlay side suggested by regression results, but tends to find fewer series exhibiting bias on the revenue side. On the other hand the nonparametric approach lends greater support to the conclusion that the government's budget projections do not fully exploit available information. Copyright 1995 by MIT Press.

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Bibliographic Info

Article provided by MIT Press in its journal Review of Economics & Statistics.

Volume (Year): 77 (1995)
Issue (Month): 1 (February)
Pages: 17-31

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Handle: RePEc:tpr:restat:v:77:y:1995:i:1:p:17-31

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Web page: http://mitpress.mit.edu/journals/

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Cited by:
  1. Elliott, Graham & Komunjer, Ivana & Timmermann, Allan G, 2003. "Estimating Loss Function Parameters," CEPR Discussion Papers, C.E.P.R. Discussion Papers 3821, C.E.P.R. Discussion Papers.
  2. Elliott, Graham & Timmermann, Allan G, 2007. "Economic Forecasting," CEPR Discussion Papers, C.E.P.R. Discussion Papers 6158, C.E.P.R. Discussion Papers.
  3. Bryan Campbell & Eric Ghysels, 1995. "An Empirical Analysis of the Canadian Budget Process," CIRANO Working Papers, CIRANO 95s-08, CIRANO.
  4. Alexander, Marcus & Christakis, Nicholas A., 2008. "Bias and asymmetric loss in expert forecasts: A study of physician prognostic behavior with respect to patient survival," Journal of Health Economics, Elsevier, Elsevier, vol. 27(4), pages 1095-1108, July.
  5. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating density forecasts," Working Papers 97-6, Federal Reserve Bank of Philadelphia.
  6. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper, Federal Reserve Bank of New York 9525, Federal Reserve Bank of New York.
  7. Elkin Castaño Vélez & Luis Fernando Melo Velandia, 2000. "Metodos de combinacion de pronosticos: una aplicacion a la inflacion," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, Universidad de Antioquia, Departamento de Economía, issue 52, pages 113-165, Enero Jun.
  8. Teresa Leal & Javier J. Pérez & Mika Tujula & Jean-Pierre Vidal, 2008. "Fiscal Forecasting: Lessons from the Literature and Challenges," Fiscal Studies, Institute for Fiscal Studies, Institute for Fiscal Studies, vol. 29(3), pages 347-386, 09.
  9. Sergey V. Chernenko, 2004. "The information content of forward and futures prices: market expectations and the price of risk," International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.) 808, Board of Governors of the Federal Reserve System (U.S.).
  10. Kitchen, John, 2003. "Observed Relationships between Economic and Technical Receipts Revisions in Federal Budget Projections," National Tax Journal, National Tax Association, National Tax Association, vol. 56(2), pages 337-53, June.
  11. Auerbach, Alan Jeffrey, 1999. "On the Performance and Use of Government Revenue Forecasts," Berkeley Olin Program in Law & Economics, Working Paper Series, Berkeley Olin Program in Law & Economics qt8h845262, Berkeley Olin Program in Law & Economics.
  12. Roland Döhrn, 2006. "Improving Business Cycle Forecasts’ Accuracy - What Can We Learn from Past Errors?," RWI Discussion Papers, Rheinisch-Westfälisches Institut für Wirtschaftsforschung 0051, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.

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