Decomposing Federal Funds Rate forecast uncertainty using real-time data

Martin Mandler ()

No 200947, MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung)

Abstract: Using real-time data I estimate out-of-sample forecast uncertainty about the Federal Funds Rate. Combining a Taylor rule with a model of economic fundamentals I disentangle economically interpretable components of forecast uncertainty: uncertainty about future economic conditions and uncertainty about future monetary policy. Uncertainty about U.S. monetary policy fell to unprecedented low levels in the 1980s and remained low while uncertainty about future output and inflation declined only temporarily. This points to an important role of increased predictability of monetary policy in explaining the decline in macroeconomic volatility in the U.S. since the mid-1980s.

Keywords: monetary policy reaction function; interest rate uncertainty; state-space model (search for similar items in EconPapers)
JEL-codes: E52 C32 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-for, nep-mac and nep-mon
Date: 2009
References: Add references at CitEc
Citations Track citations by RSS feed

Forthcoming in

Downloads: (external link)
http://www.uni-marburg.de/fb02/makro/forschung/magkspapers/47-2009_mandler.pdf Third version, 2009 (application/pdf)

Related works:
Working Paper: Decomposing Federal Funds Rate forecast uncertainty using real-time data (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:mar:magkse:200947

Access Statistics for this paper

More papers in MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung)
Contact information at EDIRC.
Series data maintained by Bernd Hayo ().