IDEAS home Printed from https://ideas.repec.org/p/fip/fedawp/99-13.html
   My bibliography  Save this paper

Prior parameter uncertainty: Some implications for forecasting and policy analysis with VAR models

Author

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

Abstract

Models used for policy analysis should generate reliable unconditional forecasts as well as policy simulations (conditional forecasts) that are based on a structural model of the economy. Vector autoregression (VAR) models have been criticized for having inaccurate forecasts as well as being difficult to interpret in the context of an underlying economic model. In this paper, we examine how the treatment of prior uncertainty about parameter values can affect forecasting accuracy and the interpretation of identified structural VAR models. ; Typically, VAR models are specified with long lag orders and a diffuse prior about the unrestricted coefficients. We find evidence that alternatives that emphasize nonstationary aspects of the data as well as parsimony in parameterization have better out-of-sample forecast performance and smoother and more persistent responses to a given exogenous monetary policy change than do unrestricted VARs.

Suggested Citation

  • John C. Robertson & Ellis W. Tallman, 1999. "Prior parameter uncertainty: Some implications for forecasting and policy analysis with VAR models," FRB Atlanta Working Paper 99-13, Federal Reserve Bank of Atlanta.
  • Handle: RePEc:fip:fedawp:99-13
    as

    Download full text from publisher

    File URL: http://www.frbatlanta.org//filelegacydocs/wp9913.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-16.
    2. 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.
    3. 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.
    4. Highfield, Richard A. & O'Hara, Maureen & Wood, John H., 1991. "Public ends, private means : Central banking and the profit motive 1823-1832," Journal of Monetary Economics, Elsevier, vol. 28(2), pages 287-322, October.
    5. Christopher A. Sims & Tao Zha, 1999. "Error Bands for Impulse Responses," Econometrica, Econometric Society, vol. 67(5), pages 1113-1156, September.
    6. Robert B. Litterman, 1979. "Techniques of forecasting using vector autoregressions," Working Papers 115, Federal Reserve Bank of Minneapolis.
    7. 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.
    8. 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.
    9. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    10. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    11. Adrian R. Pagan & John C. Robertson, 1995. "Resolving the liquidity effect," Proceedings, Federal Reserve Bank of St. Louis, issue May, pages 33-54.
    12. Robertson, John C & Tallman, Ellis W, 2001. "Improving Federal-Funds Rate Forecasts in VAR Models Used for Policy Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 324-330, July.
    13. 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.
    14. Gordon, David B & Leeper, Eric M, 1994. "The Dynamic Impacts of Monetary Policy: An Exercise in Tentative Identification," Journal of Political Economy, University of Chicago Press, vol. 102(6), pages 1228-1247, December.
    15. Jon Faust, 1998. "The robustness of identified VAR conclusions about money," International Finance Discussion Papers 610, Board of Governors of the Federal Reserve System (U.S.).
    16. Eric M. Leeper & Christopher A. Sims & Tao Zha, 1996. "What Does Monetary Policy Do?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(2), pages 1-78.
    17. 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.
    18. Canova, Fabio, 1991. "The Sources of Financial Crisis: Pre- and Post-Fed Evidence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 32(3), pages 689-713, August.
    19. Faust, Jon, 1998. "The robustness of identified VAR conclusions about money," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 207-244, December.
    20. 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.
    21. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
    22. 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.
    23. 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.
    24. James E. Kennedy, 1989. "The effect of Bayesian priors on the moving-average representation of vector autoregressions," Finance and Economics Discussion Series 79, Board of Governors of the Federal Reserve System (U.S.).
    25. Christopher A. Sims, 1998. "Role of interest rate policy in the generation and propagation of business cycles: what has changed since the '30s?," Conference Series ; [Proceedings], Federal Reserve Bank of Boston, vol. 42(Jun), pages 121-175.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Summers, Peter M., 2001. "Forecasting Australia's economic performance during the Asian crisis," International Journal of Forecasting, Elsevier, vol. 17(3), pages 499-515.
    2. Kaabia, Olfa & Abid, Ilyes & Mkaouar, Farid, 2016. "The dark side of the black gold shock onto Europe: One stock's joy is another stock's sorrow," Economic Modelling, Elsevier, vol. 58(C), pages 642-654.
    3. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    4. Kaabia, Olfa & Abid, Ilyes & Guesmi, Khaled, 2013. "Does Bayesian shrinkage help to better reflect what happened during the subprime crisis?," Economic Modelling, Elsevier, vol. 31(C), pages 423-432.
    5. Nurmakhanova Mira, 2016. "Oil and Growth Challenge in Kazakhstan," EERC Working Paper Series 16/06e, EERC Research Network, Russia and CIS.
    6. Andrea Brischetto & Graham Voss, 1999. "A Structural Vector Autoregression Model of Monetary Policy in Australia," RBA Research Discussion Papers rdp1999-11, Reserve Bank of Australia.

    More about this item

    Keywords

    Forecasting ; Vector autoregression ; Econometric models;

    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:99-13. 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.