Bayesian forecasting of federal funds target rate decisions

Sjoerd van den Hauwe, Richard Paap and Dick van Dijk

Journal of Macroeconomics, 2013, vol. 37, issue C, 19-40

Abstract: In this paper we examine which macroeconomic and financial variables have most predictive ability for the federal funds target rate decisions made by the Federal Open Market Committee (FOMC). We conduct the analysis for the 157 FOMC decisions during the period January 1990–June 2008, using dynamic ordered probit models with a Bayesian endogenous variable selection methodology and real-time data for a set of 33 candidate predictor variables. We find that indicators of economic activity and forward-looking term structure variables, as well as survey measures are most informative from a forecasting perspective. For the full sample period, in-sample probability forecasts achieve a hit rate of 90%. Based on out-of-sample forecasts for the period January 2001–June 2008, 82% of the FOMC decisions are predicted correctly.

Keywords: Federal funds target rate; Real-time forecasting; Dynamic ordered probit; Variable selection; Bayesian analysis; Importance sampling (search for similar items in EconPapers)
JEL-codes: C11 C25 C53 E52 E58 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0164070413000876
Full text for ScienceDirect subscribers only

Related works:
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: https://EconPapers.repec.org/RePEc:eee:jmacro:v:37:y:2013:i:c:p:19-40

DOI: 10.1016/j.jmacro.2013.05.001

Access Statistics for this article

Journal of Macroeconomics is currently edited by Douglas McMillin and Theodore Palivos

More articles in Journal of Macroeconomics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().