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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. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
  2. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, . "Evaluating Density Forecasts," CARESS Working Papres 97-18, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
  3. Bryan Campbell & Eric Ghysels, 1997. "An Empirical Analysis of the Canadian Budget Process," Canadian Journal of Economics, Canadian Economics Association, vol. 30(3), pages 553-76, August.
  4. Kitchen, John, 2003. "Observed Relationships Between Economic And Technical Receipts Revisions In Federal Budget Projections," MPRA Paper 22004, University Library of Munich, Germany.
  5. Elliott, Graham & Timmermann, Allan G, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
  6. Sergey V. Chernenko, 2004. "The information content of forward and futures prices: market expectations and the price of risk," International Finance Discussion Papers 808, Board of Governors of the Federal Reserve System (U.S.).
  7. Elliott, Graham & Komunjer, Ivana & Timmermann, Allan G, 2003. "Estimating Loss Function Parameters," CEPR Discussion Papers 3821, C.E.P.R. Discussion Papers.
  8. 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, vol. 27(4), pages 1095-1108, July.
  9. 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, vol. 29(3), pages 347-386, 09.
  10. Roland Döhrn, 2006. "Improving Business Cycle Forecasts’ Accuracy - What Can We Learn from Past Errors?," RWI Discussion Papers 0051, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
  11. Auerbach, Alan J., 1999. "On the Performance and Use of Government Revenue Forecasts," National Tax Journal, National Tax Association, vol. 52(n. 4), pages 765-82, December.
  12. 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, issue 52, pages 113-165, Enero Jun.

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