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Improving Federal-Funds Rate Forecasts in VAR Models Used for Policy Analysis

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  • Robertson, John C
  • Tallman, Ellis W

Abstract

Federal-funds rate-forecast errors from vector autoregressive (VAR) models used for monetary policy analysis and fitted by ordinary least squares (OLS) are large relative to those from the futures market. Using three different structural VAR models, we show that forecasts based on a shrinkage estimator dominate the OLS-based forecasts--even after restricting the lag length and/or imposing exact unit-root restrictions--and are broadly comparable to the futures-market forecasts. Our results refute the perception that VAR models forecast the funds rate poorly in general and suggest that using stochastic prior restrictions can provide an effective way of improving forecast accuracy without sacrificing structural interpretation.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 19 (2001)
Issue (Month): 3 (July)
Pages: 324-30

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Handle: RePEc:bes:jnlbes:v:19:y:2001:i:3:p:324-30

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  1. Christopher A. Sims, 1989. "A nine variable probabilistic macroeconomic forecasting model," Discussion Paper / Institute for Empirical Macroeconomics 14, Federal Reserve Bank of Minneapolis.
  2. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
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  9. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
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  12. repec:wop:humbsf:1999-4 is not listed on IDEAS
  13. Kadiyala, K. Rao & Karlsson, Sune, 1994. "Numerical Aspects of Bayesian VAR-modeling," Working Paper Series in Economics and Finance 12, Stockholm School of Economics.
  14. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
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