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Comment on “How Biased are US Government Forecasts of the Federal Debt?”


  • Gamber, Edward N.
  • Liebner, Jeffrey P.


In this comment on “How Biased are US Government Forecasts of the Federal Debt?” by Neil R. Ericsson, we investigate the sensitivity of the “bare-bones” application of the impulse indicator saturation technique. We offer an alternative but complementary interpretation of Ericsson’s findings of bias in government debt forecasts. Our findings reinforce his interpretation of the role of the IIS technique as a general diagnostic tool for detecting model misspecification.

Suggested Citation

  • Gamber, Edward N. & Liebner, Jeffrey P., 2017. "Comment on “How Biased are US Government Forecasts of the Federal Debt?”," International Journal of Forecasting, Elsevier, vol. 33(2), pages 560-562.
  • Handle: RePEc:eee:intfor:v:33:y:2017:i:2:p:560-562
    DOI: 10.1016/j.ijforecast.2014.11.003

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    References listed on IDEAS

    1. Andrew B. Martinez, 2011. "Comparing Government Forecasts of the United States’ Gross Federal Debt," Working Papers 2011-002, The George Washington University, Department of Economics, Research Program on Forecasting.
    2. Marcelle Chauvet & Jeremy M. Piger, 2003. "Identifying business cycle turning points in real time," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 47-61.
    3. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
    4. Robert H. Rasche, 1985. "Deficit projections vs. deficit forecasts," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue jul5.
    5. David Hendry & Carlos Santos, 2010. "An Automatic Test of Super Exogeneity," Economics Series Working Papers 476, University of Oxford, Department of Economics.
    6. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
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    Cited by:

    1. Ericsson, Neil R., 2017. "Interpreting estimates of forecast bias," International Journal of Forecasting, Elsevier, vol. 33(2), pages 563-568.


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