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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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  • Campbell, Bryan & Ghysels, Eric, 1995. "Federal Budget Projections: A Nonparametric Assessment of Bias and Efficiency," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 17-31, February.
  • Handle: RePEc:tpr:restat:v:77:y:1995:i:1:p:17-31
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    Cited by:

    1. 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, September.
    2. Cronin, David & McQuinn, Kieran, 2020. "Are official forecasts of output growth in the EU still biased? Evidence from stability and convergence programmes and the European Commission’s Spring forecasts," Papers WP681, Economic and Social Research Institute (ESRI).
    3. Wieland, Volker & Wolters, Maik, 2013. "Forecasting and Policy Making," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 239-325, Elsevier.
    4. Auerbach, Alan J., 1999. "On the Performance and Use of Government Revenue Forecasts," National Tax Journal, National Tax Association;National Tax Journal, vol. 52(4), pages 765-782, December.
    5. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, "undated". "Evaluating Density Forecasts," CARESS Working Papres 97-18, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    6. Björn Kauder & Niklas Potrafke & Christoph Schinke, 2017. "Manipulating Fiscal Forecasts: Evidence from the German States," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 73(2), pages 213-236, June.
    7. Natsuki Arai, 2016. "Evaluating the Efficiency of the FOMC's New Economic Projections," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(5), pages 1019-1049, August.
    8. Ulrich Fritsche & Artur Tarassow, 2017. "Vergleichende Evaluation der Konjunkturprognosen des Instituts für Makroökonomie und Konjunkturforschung an der Hans-Böckler-Stiftung für den Zeitraum 2005-2014," IMK Studies 54-2017, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    9. Elliott, Graham & Komunjer, Ivana & Timmermann, Allan G, 2003. "Estimating Loss Function Parameters," CEPR Discussion Papers 3821, C.E.P.R. Discussion Papers.
    10. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    11. Cronin, David & McQuinn, Kieran, 2021. "Are official forecasts of output growth in the EU still biased?," Journal of Policy Modeling, Elsevier, vol. 43(2), pages 337-349.
    12. Vasconcelos de Deus, Joseph David Barroso & de Mendonça, Helder Ferreira, 2017. "Fiscal forecasting performance in an emerging economy: An empirical assessment of Brazil," Economic Systems, Elsevier, vol. 41(3), pages 408-419.
    13. Dean Croushore & Simon van Norden, 2018. "Fiscal Forecasts at the FOMC: Evidence from the Greenbooks," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 933-945, December.
    14. Zidong An & Joao Tovar Jalles, 2020. "On the performance of US fiscal forecasts: government vs. private information," Journal of Economic Studies, Emerald Group Publishing, vol. 48(2), pages 367-391, June.
    15. Croushore, Dean & van Norden, Simon, 2019. "Fiscal Surprises at the FOMC," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1583-1595.
    16. Kitchen, John, 2003. "Observed Relationships Between Economic and Technical Receipts Revisions in Federal Budget Projections," National Tax Journal, National Tax Association;National Tax Journal, vol. 56(2), pages 337-353, June.
    17. 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.).
    18. Bryan Campbell & Eric Ghysels, 1997. "An Empirical Analysis of the Canadian Budget Process," Canadian Journal of Economics, Canadian Economics Association, vol. 30(3), pages 553-576, August.
    19. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    20. Peter, Eckley, 2015. "(Non)rationality of consumer inflation perceptions," MPRA Paper 77082, University Library of Munich, Germany.
    21. 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.
    22. 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.

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