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How biased are U.S. government forecasts of the federal debt?

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  • Ericsson, Neil R.

Abstract

Government debt and its forecasts attracted considerable attention during the recent financial crisis. The current paper analyzes potential biases in different U.S. government agencies’ one-year-ahead forecasts of U.S. gross federal debt over 1984–2012. Standard tests typically fail to detect biases in these forecasts. However, impulse indicator saturation (IIS) detects economically large and highly significant time-varying biases, particularly at turning points in the business cycle. These biases do not appear to be politically related. IIS defines a generic procedure for examining forecast properties; it explains why standard tests fail to detect bias; and it provides a mechanism for potentially improving forecasts.

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  • Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
  • Handle: RePEc:eee:intfor:v:33:y:2017:i:2:p:543-559
    DOI: 10.1016/j.ijforecast.2016.09.001
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    Cited by:

    1. Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
    2. Ericsson, Neil R., 2017. "Interpreting estimates of forecast bias," International Journal of Forecasting, Elsevier, vol. 33(2), pages 563-568.

    More about this item

    Keywords

    Autometrics; Bias; Debt; Federal government; Forecasts; Impulse indicator saturation; Projections; United States;

    JEL classification:

    • H68 - Public Economics - - National Budget, Deficit, and Debt - - - Forecasts of Budgets, Deficits, and Debt
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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