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What Can We Learn From Revisions to the Greenbook Forecasts?

Author

Listed:
  • Tara M. Sinclair

    (Department of Economics/Institute for International Economic Policy, George Washington University)

  • Jeff Messina

    (Department of Economics/Institute for International Economic Policy, George Washington University)

  • Herman Stekler

    (Department of Economics, George Washington University)

Abstract

Although there have been many evaluations of the Fed Greenbook forecasts, we analyze them in a different dimension. We examine the revisions of these forecasts in the context of fixed event predictions to determine how new information is incorporated in the forecasting process. This analysis permits us to determine whether there was an underutilization of information. There is no evidence of forecast smoothing, but rather that the revisions were sometimes in the wrong direction.

Suggested Citation

  • Tara M. Sinclair & Jeff Messina & Herman Stekler, 2014. "What Can We Learn From Revisions to the Greenbook Forecasts?," Working Papers 2014-14, The George Washington University, Institute for International Economic Policy.
    • Handle: RePEc:gwi:wpaper:2014-14
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    2. Andrew C. Chang & Trace J. Levinson, 2023. "Raiders of the lost high‐frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 88-104, January.
    3. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
    4. Constantin Bürgi, 2020. "Expectation Formation and the Persistence of Shocks," Working Papers 2020-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Sep 2020.
    5. Katharina Glass & Ulrich Fritsche, 2015. "Real-time Macroeconomic Data and Uncertainty," Macroeconomics and Finance Series 201406, University of Hamburg, Department of Socioeconomics.
    6. Granziera, Eleonora & Jalasjoki, Pirkka & Paloviita, Maritta, 2024. "The bias of the ECB inflation projections: A State-dependent analysis," Bank of Finland Research Discussion Papers 4/2024, Bank of Finland.
    7. Pierre L. Siklos, 2020. "U.S. Monetary Policy since the 1950s and the Changing Content of FOMC Minutes," Southern Economic Journal, John Wiley & Sons, vol. 86(3), pages 1192-1213, January.
    8. Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2021. "Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 290-314.
    9. Berge, Travis J. & Chang, Andrew C. & Sinha, Nitish R., 2019. "Evaluating the conditionality of judgmental forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1627-1635.
    10. Andrew C. Chang & Trace J. Levinson, 2020. "Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting," Finance and Economics Discussion Series 2020-090, Board of Governors of the Federal Reserve System (U.S.).
    11. Dovern, Jonas & Jannsen, Nils, 2017. "Systematic errors in growth expectations over the business cycle," International Journal of Forecasting, Elsevier, vol. 33(4), pages 760-769.
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    13. Jacobs, Jan P.A.M. & van Norden, Simon, 2016. "Why are initial estimates of productivity growth so unreliable?," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 200-213.
    14. Yoichi Tsuchiya, 2021. "The value added of the Bank of Japan's range forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 817-833, August.
    15. Lixiong Yang, 2020. "State-dependent biases and the quality of China’s preliminary GDP announcements," Empirical Economics, Springer, vol. 59(6), pages 2663-2687, December.
    16. G. Kontogeorgos & K. Lambrias, 2022. "Evaluating the Eurosystem/ECB staff macroeconomic projections: The first 20 years," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 213-229, March.
    17. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    18. 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.
    19. Eleonora Granziera & Pirkka Jalasjoki & Maritta Paloviita, 2021. "The Bias and Efficiency of the ECB Inflation Projections: a State Dependent Analysis," Working Paper 2021/1, Norges Bank.
    20. Kuethe, Todd H. & Hubbs, Todd & Sanders, Dwight R., 2018. "Evaluating the USDA’s Net Farm Income Forecast," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(3), September.
    21. Travis J. Berge, 2023. "Time-Varying Uncertainty of the Federal Reserve's Output Gap Estimate," The Review of Economics and Statistics, MIT Press, vol. 105(5), pages 1191-1206, September.
    22. Tara M. Sinclair, 2019. "Continuities and Discontinuities in Economic Forecasting," Working Papers 2019-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    23. Sergey V. Smirnov & Daria A. Avdeeva, 2016. "Wishful Bias in Predicting Us Recessions: Indirect Evidence," HSE Working papers WP BRP 135/EC/2016, National Research University Higher School of Economics.
    24. Granziera, Eleonora & Jalasjoki, Pirkka & Paloviita, Maritta, 2021. "The bias and efficiency of the ECB inflation projections: A state dependent analysis," Bank of Finland Research Discussion Papers 7/2021, Bank of Finland.
    25. 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; Forecast Revisions;
    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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