IDEAS home Printed from https://ideas.repec.org/p/fip/fedawp/99-3.html
   My bibliography  Save this paper

Improving forecasts of the federal funds rate in a policy model

Author

Listed:
  • John C. Robertson
  • Ellis W. Tallman

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.

Suggested Citation

  • John C. Robertson & Ellis W. Tallman, 1999. "Improving forecasts of the federal funds rate in a policy model," FRB Atlanta Working Paper 99-3, Federal Reserve Bank of Atlanta.
  • Handle: RePEc:fip:fedawp:99-3
    as

    Download full text from publisher

    File URL: http://www.frbatlanta.org//filelegacydocs/wp9903.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Dreze, Jacques H. & Richard, Jean-Francois, 1983. "Bayesian analysis of simultaneous equation systems," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 9, pages 517-598 Elsevier.
    2. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148 Elsevier.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    5. 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.
    6. Charles L. Evans & Kenneth N. Kuttner, 1998. "Can VAR's describe monetary policy?," Working Paper Series WP-98-19, Federal Reserve Bank of Chicago.
    7. Christopher A. Sims, 1993. "A Nine-Variable Probabilistic Macroeconomic Forecasting Model," NBER Chapters,in: Business Cycles, Indicators and Forecasting, pages 179-212 National Bureau of Economic Research, Inc.
    8. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    9. John C. Robertson & Ellis W. Tallman, 1998. "Data vintages and measuring forecast model performance," Economic Review, Federal Reserve Bank of Atlanta, issue Q 4, pages 4-20.
    10. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-389, June.
    11. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    12. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
    13. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    14. Rudebusch, Glenn D, 1998. "Do Measures of Monetary Policy in a VAR Make Sense?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 907-931, November.
    15. Tao Zha, 1998. "A dynamic multivariate model for use in formulating policy," Economic Review, Federal Reserve Bank of Atlanta, issue Q 1, pages 16-29.
    16. Wallis, Kenneth F, 1989. "Macroeconomic Forecasting: A Survey," Economic Journal, Royal Economic Society, vol. 99(394), pages 28-61, March.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Forecasting ; Federal funds rate ; Vector autoregression;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedawp:99-3. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Elaine Clokey). General contact details of provider: http://edirc.repec.org/data/frbatus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.