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Evaluating a Vector of the Fed's Forecasts

Author

Listed:
  • Tara Sinclair

    (Institute for International Economic Policy, George Washington University)

  • Herman O. Stekler

    (Department of Economics, George Washington University)

  • Warren Carrow

    (Economics Department, George Washington University)

Abstract
In this paper we present a multivariate analysis of the Federal Reserve’s forecasts. First, we evaluate the Fed’s forecasts of the ten major expenditure categories of real GDP. Second, we present a new methodology for evaluating multivariate forecasts. Finally, we use the same methodology to determine whether the Fed’s forecasts of GDP growth, inflation, and unemployment taken together present an accurate overall view of the economic situation and compare the Fed’s forecasts to those of the Survey of Professional Forecasters. We find that the Fed’s forecasts were generally consistent with the overall conditions that actually occurred. We also find that the Fed’s forecasts and those of the Survey of Professional Forecasters are quite similar overall.

Suggested Citation

  • Tara Sinclair & Herman O. Stekler & Warren Carrow, 2012. "Evaluating a Vector of the Fed's Forecasts," Working Papers 2012-3, The George Washington University, Institute for International Economic Policy.
    • Handle: RePEc:gwi:wpaper:2012-3
    as

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    File URL: http://www.gwu.edu/~iiep/assets/docs/papers/Sinclair_IIEPWP2012-03.pdf
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    References listed on IDEAS

    as
    1. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    2. Sinclair, Tara M. & Joutz, Fred & Stekler, H.O., 2010. "Can the Fed predict the state of the economy?," Economics Letters, Elsevier, vol. 108(1), pages 28-32, July.
    3. Ivana Komunjer & Michael T. Owyang, 2012. "Multivariate Forecast Evaluation and Rationality Testing," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1066-1080, November.
    4. Tara M. Sinclair & H. O. Stekler & Warren Carnow, 2012. "A new approach for evaluating economic forecasts," Economics Bulletin, AccessEcon, vol. 32(3), pages 2332-2342.
    5. 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.
    6. Sinclair, Tara M. & Stekler, H.O., 2013. "Examining the quality of early GDP component estimates," International Journal of Forecasting, Elsevier, vol. 29(4), pages 736-750.
    7. Sinclair, Tara M. & Gamber, Edward N. & Stekler, Herman & Reid, Elizabeth, 2012. "Jointly evaluating the Federal Reserve’s forecasts of GDP growth and inflation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 309-314.
    8. Fred Joutz & Michael P. Clements & Herman O. Stekler, 2007. "An evaluation of the forecasts of the federal reserve: a pooled approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 121-136.
    9. Hsiao,Cheng & Pesaran,M. Hashem & Lahiri,Kajal & Lee,Lung Fei (ed.), 1999. "Analysis of Panels and Limited Dependent Variable Models," Cambridge Books, Cambridge University Press, number 9780521631693, September.
    10. Jordà, Òscar & Knüppel, Malte & Marcellino, Massimiliano, 2013. "Empirical simultaneous prediction regions for path-forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 456-468.
    11. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.
    12. Kajal Lahiri & Antony Davies & Xuguang Sheng, 2010. "Analyzing Three-Dimensional Panel Data of Forecasts," Discussion Papers 10-07, University at Albany, SUNY, Department of Economics.
    13. 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.
    14. Holden, K & Peel, D A, 1990. "On Testing for Unbiasedness and Efficiency of Forecasts," The Manchester School of Economic & Social Studies, University of Manchester, vol. 58(2), pages 120-127, June.
    15. Robert A. Eisenbeis & Daniel F. Waggoner & Tao Zha, 2002. "Evaluating Wall Street Journal survey forecasters: a multivariate approach," FRB Atlanta Working Paper 2002-8, Federal Reserve Bank of Atlanta.
    16. Tara Sinclair & H. O. Stekler & L. Kitzinger, 2010. "Directional forecasts of GDP and inflation: a joint evaluation with an application to Federal Reserve predictions," Applied Economics, Taylor & Francis Journals, vol. 42(18), pages 2289-2297.
    17. Julieta Caunedo & Riccardo Dicecio & Ivana Komunjer & Michael T. Owyang, 2020. "Asymmetry, Complementarities, and State Dependence in Federal Reserve Forecasts," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(1), pages 205-228, February.
    18. Joutz, Fred & Stekler, H. O., 2000. "An evaluation of the predictions of the Federal Reserve," International Journal of Forecasting, Elsevier, vol. 16(1), pages 17-38.
    19. David L. Reifschneider & Peter Tulip, 2007. "Gauging the uncertainty of the economic outlook from historical forecasting errors," Finance and Economics Discussion Series 2007-60, Board of Governors of the Federal Reserve System (U.S.).
    20. Chanont Banternghansa & Michael W. McCracken, 2009. "Forecast disagreement among FOMC members," Working Papers 2009-059, Federal Reserve Bank of St. Louis.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Hendry, David F. & Martinez, Andrew B., 2017. "Evaluating multi-step system forecasts with relatively few forecast-error observations," International Journal of Forecasting, Elsevier, vol. 33(2), pages 359-372.
    2. Kung, Ko-Lun & MacMinn, Richard D. & Kuo, Weiyu & Tsai, Chenghsien Jason, 2022. "Multi-population mortality modeling: When the data is too much and not enough," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 41-55.
    3. Ericsson, Neil R., 2016. "Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis," International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
    4. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    5. Tara M. Sinclair & H. O. Stekler & Warren Carnow, 2012. "A new approach for evaluating economic forecasts," Economics Bulletin, AccessEcon, vol. 32(3), pages 2332-2342.
    6. Binder, Carola Conces & Wetzel, Samantha, 2018. "The FOMC versus the staff, revisited: When do policymakers add value?," Economics Letters, Elsevier, vol. 171(C), pages 72-75.
    7. Xie, Zixiong & Hsu, Shih-Hsun, 2016. "Time varying biases and the state of the economy," International Journal of Forecasting, Elsevier, vol. 32(3), pages 716-725.
    8. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.

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    More about this item

    Keywords

    Federal Reserve; Forecast Evaluation; real GDP Expenditure Approach; Business Cycle; Mahalanobis Distance;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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