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An Evaluation of CBO Forecasts: Working Paper 2009-02

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  • Jon Huntley
  • Eric Miller

Abstract

We compare the performance of a subset of CBO's economic forecasts against that of an unrestricted vector autoregression (VAR) model. We evaluate forecasts of real economic indicators as well as budget-related nominal statistics. We find that under most specifications, the VAR performs competitively with, if slightly worse than, the corresponding CBO forecasts at up to 20 quarters. Therefore, a simple VAR is unlikely to be able to contribute directly to improving budget forecasts. The only series for which the VAR outperforms the CBO forecast is the growth in real

Suggested Citation

  • Jon Huntley & Eric Miller, 2009. "An Evaluation of CBO Forecasts: Working Paper 2009-02," Working Papers 41195, Congressional Budget Office.
    • Handle: RePEc:cbo:wpaper:41195
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    File URL: https://www.cbo.gov/sites/default/files/111th-congress-2009-2010/workingpaper/2009-02_0.pdf
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    References listed on IDEAS

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    1. Wendy Edelberg & Martin Eichenbaum & Jonas D.M. Fisher, 1999. "Understanding the Effects of a Shock to Government Purchases," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 2(1), pages 166-206, January.
    2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    3. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    4. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    5. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    6. Rochelle M. Edge & Michael T. Kiley & Jean-Philippe Laforte, 2010. "A comparison of forecast performance between Federal Reserve staff forecasts, simple reduced-form models, and a DSGE model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 720-754.
    7. Andrew Bauer & Robert A. Eisenbeis & Daniel F. Waggoner & Tao Zha, 2003. "Forecast evaluation with cross-sectional data: The Blue Chip Surveys," Economic Review, Federal Reserve Bank of Atlanta, vol. 88(Q2), pages 17-31.
    8. David Laster & Paul Bennett & In Sun Geoum, 1999. "Rational Bias in Macroeconomic Forecasts," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(1), pages 293-318.
    9. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
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