How Biased Are U.S. Government Forecasts of the Federal Debt?
Neil Ericsson ()
No 2017-001, Working Papers from The George Washington University, Department of Economics, Research Program on Forecasting
Government debt and forecasts thereof 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.
Keywords: Autometrics; bias; debt; federal government; forecasts; impulse indicator saturation; heteroscedasticity; projections; United States. (search for similar items in EconPapers)
JEL-codes: H68 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-mac
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http://www.gwu.edu/~forcpgm/2017-001.pdf First version, 2017 (application/pdf)
Journal Article: How biased are U.S. government forecasts of the federal debt? (2017)
Working Paper: How Biased Are U.S. Government Forecasts of the Federal Debt? (2017)
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