How Biased Are U.S. Government Forecasts of the Federal Debt?
Neil Ericsson ()
No 1189, International Finance Discussion Papers from Board of Governors of the Federal Reserve System (U.S.)
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: Heteroscedasticity; Forecasts; Bias; Federal government; Debt; Projections; Autometrics; Impulse indicator saturation; United States (search for similar items in EconPapers)
JEL-codes: C53 H68 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2017-01-06, Revised 2017-01-06
New Economics Papers: this item is included in nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
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)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgif:1189
Access Statistics for this paper
More papers in International Finance Discussion Papers from Board of Governors of the Federal Reserve System (U.S.) Contact information at EDIRC.
Bibliographic data for series maintained by Ryan Wolfslayer ; Keisha Fournillier ().