Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/100803
Authors: 
Robertson, John C.
Tallman, Ellis W.
Year of Publication: 
1999
Series/Report no.: 
Working Paper, Federal Reserve Bank of Atlanta 99-3
Abstract: 
Vector autoregression (VAR) models are widely used for policy analysis. Some authors caution, however, that the forecast errors of the federal funds rate from such a VAR are large compared to those from the federal funds futures market. From these findings, it is argued that the inaccurate federal funds rate forecasts from VARs limit their usefulness as a tool for guiding policy decisions. In this paper, we demonstrate that the poor forecast performance is largely eliminated if a Bayesian estimation technique is used instead of OLS. In particular, using two different data sets we show that the forecasts from the Bayesian VAR dominate the forecasts from OLS VAR models—even after imposing various exact exclusion restrictions on lags and levels of the data.
Subjects: 
Forecasting
Federal funds rate
Vector autoregression
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.