IDEAS home Printed from https://ideas.repec.org/a/sae/amerec/v55y2010i1p105-110.html
   My bibliography  Save this article

Evidence on Forecasting Inflation Under Asymmetric Loss

Author

Listed:
  • Hamid Baghestani
  • Bassam Abual-Foul

Abstract

This study extends previous work on the asymmetric information hypothesis by comparing the Federal Reserve and private inflation forecasts in terms of directional accuracy for 1983–2002. In support of this hypothesis, the Federal Reserve forecasts show superiority in terms of both predictive content and directional accuracy. However, both sets of forecasts are far more accurate in predicting upward moves than they are in predicting downward moves. In an environment where maintaining price stability is a top priority, we interpret such evidence as preference for over-prediction under asymmetric loss and argue that the bias in the inflation forecasts is rational.

Suggested Citation

  • Hamid Baghestani & Bassam Abual-Foul, 2010. "Evidence on Forecasting Inflation Under Asymmetric Loss," The American Economist, Sage Publications, vol. 55(1), pages 105-110, May.
    • Handle: RePEc:sae:amerec:v:55:y:2010:i:1:p:105-110
    DOI: 10.1177/056943451005500111
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/056943451005500111
    Download Restriction: no
    File URL: https://libkey.io/10.1177/056943451005500111?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ruge-Murcia, Francisco J, 2003. "Inflation Targeting under Asymmetric Preferences," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 35(5), pages 763-785, October.
    2. Christopher A. Sims, 2002. "The Role of Models and Probabilities in the Monetary Policy Process," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 33(2), pages 1-62.
    3. Sandra Pianalto, 2007. "Price stability: issues and challenges," Economic Commentary, Federal Reserve Bank of Cleveland, issue Jun.
    4. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    5. 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.
    6. Alvaro Aguiar & Manuel Martins, 2008. "Testing for asymmetries in the preferences of the euro-area monetary policymaker," Applied Economics, Taylor & Francis Journals, vol. 40(13), pages 1651-1667.
    7. Francisco J. Ruge-Murcia, 2000. "Uncovering financial markets' beliefs about inflation targets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 483-512.
    8. David H. Romer & Christina D. Romer, 2000. "Federal Reserve Information and the Behavior of Interest Rates," American Economic Review, American Economic Association, vol. 90(3), pages 429-457, June.
    9. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    10. Hamid Baghestani & Mohamed Soliman, 2009. "Federal Reserve vs. consumer forecasts of inflation: 1979-1983," Applied Economics Letters, Taylor & Francis Journals, vol. 16(15), pages 1565-1568.
    11. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-590, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Capistrán, Carlos, 2008. "Bias in Federal Reserve inflation forecasts: Is the Federal Reserve irrational or just cautious?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1415-1427, November.
    2. George B. Tawadros, 2013. "The information content of the Reserve Bank of Australia's inflation forecasts," Applied Economics, Taylor & Francis Journals, vol. 45(5), pages 623-628, February.
    3. Hamid Baghestani, 2011. "A directional analysis of Federal Reserve predictions of growth in unit labor costs and productivity," International Review of Applied Economics, Taylor & Francis Journals, vol. 25(3), pages 303-311.
    4. Baghestani, Hamid & Khallaf, Ashraf, 2012. "Predictions of growth in U.S. corporate profits: Asymmetric vs. symmetric loss," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 222-229.
    5. Baghestani, Hamid, 2011. "Federal Reserve and private forecasts of growth in investment," Journal of Economics and Business, Elsevier, vol. 63(4), pages 290-305, July.
    6. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    7. Hamid Baghestani, 2010. "Evaluating Blue Chip forecasts of the trade-weighted dollar exchange rate," Applied Financial Economics, Taylor & Francis Journals, vol. 20(24), pages 1879-1889.
    8. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    9. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 17-33, March.
    10. Rochelle M. Edge & Michael T. Kiley & Jean-Philippe Laforte, 2010. "A comparison of forecast performance between Federal Reserve staff forecasts, simple reduced-form models, and a DSGE model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 720-754.
    11. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    12. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    13. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    14. Fabian Krüger & Todd E. Clark & Francesco Ravazzolo, 2017. "Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 470-485, July.
    15. Hamid Baghestani, 2014. "On the loss structure of federal reserve forecasts of output growth," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 38(3), pages 518-527, July.
    16. Baghestani, Hamid & AbuAl-Foul, Bassam M., 2017. "Comparing Federal Reserve, Blue Chip, and time series forecasts of US output growth," Journal of Economics and Business, Elsevier, vol. 89(C), pages 47-56.
    17. Wu, Yih-Jiuan, 1998. "Exchange rate forecasting: an application of radial basis function neural networks," ISU General Staff Papers 1998010108000013540, Iowa State University, Department of Economics.
    18. Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).
    19. Dal Bianco, Marcos & Camacho, Maximo & Perez Quiros, Gabriel, 2012. "Short-run forecasting of the euro-dollar exchange rate with economic fundamentals," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 377-396.
    20. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.

    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:sae:amerec:v:55:y:2010:i:1:p:105-110. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: http://journals.sagepub.com/home/aex .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.