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A New Approach For Evaluating Economic Forecasts

Author

Listed:
  • Tara Sinclair

    (Institute for International Economic Policy, George Washington University)

  • Herman O. Stekler

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

  • Warren Carnow

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

Abstract

This paper presents a new approach to evaluating multiple economic forecasts. In the past, evaluations have focused on the forecasts of individual variables. However, many macroeconomic variables are forecast at the same time and are used together to describe the state of the economy. It is, therefore, appropriate to examine those forecasts jointly. This specific approach is based on the Sinclair and Stekler (forthcoming) analysis of data revisions. The main contributions of this paper are (1) the application of this technique to the Survey of Professional Forecasters (SPF) and (2) showing that there is a bias that is associated with the stages of the business cycle.

Suggested Citation

  • Tara Sinclair & Herman O. Stekler & Warren Carnow, 2012. "A New Approach For Evaluating Economic Forecasts," Working Papers 2012-2, The George Washington University, Institute for International Economic Policy.
    • Handle: RePEc:gwi:wpaper:2012-2
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    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. Behrens, Christoph, 2019. "Evaluating the Joint Efficiency of German Trade Forecasts. A nonparametric multivariate approach," Working Papers 9, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    3. An, Zidong & Ball, Laurence & Jalles, Joao & Loungani, Prakash, 2019. "Do IMF forecasts respect Okun’s law? Evidence for advanced and developing economies," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1131-1142.
    4. Ball, Laurence & Jalles, João Tovar & Loungani, Prakash, 2015. "Do forecasters believe in Okun’s Law? An assessment of unemployment and output forecasts," International Journal of Forecasting, Elsevier, vol. 31(1), pages 176-184.
    5. Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
    6. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    7. Sinclair, Tara M. & Stekler, H.O. & Carnow, Warren, 2015. "Evaluating a vector of the Fed’s forecasts," International Journal of Forecasting, Elsevier, vol. 31(1), pages 157-164.
    8. 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.
    9. Kim, Jong Min & Jun, Mina & Kim, Chung K., 2018. "The Effects of Culture on Consumers' Consumption and Generation of Online Reviews," Journal of Interactive Marketing, Elsevier, vol. 43(C), pages 134-150.
    10. Glas, Alexander & Heinisch, Katja, 2021. "Conditional macroeconomic forecasts: Disagreement, revisions and forecast errors," IWH Discussion Papers 7/2021, Halle Institute for Economic Research (IWH).
    11. Stekler, Herman & Symington, Hilary, 2016. "Evaluating qualitative forecasts: The FOMC minutes, 2006–2010," International Journal of Forecasting, Elsevier, vol. 32(2), pages 559-570.
    12. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.
    13. Döhrn, Roland, 2015. "Der Prognostiker des Jahres: Ein Zufallsergebnis? Möglichkeiten einer mehrdimensionalen Evaluierung von Konjunkturprognosen," IBES Diskussionsbeiträge 208, University of Duisburg-Essen, Institute of Business and Economic Studie (IBES).
    14. Herman O. Stekler & Hilary Symington, 2014. "How Did The Fomc View The Great Recession As It Was Happening?: Evaluating The Minutes From Fomc Meetings, 2006-2010," Working Papers 2014-005, The George Washington University, The Center for Economic Research.
    15. Eicher, Theo S. & Rollinson, Yuan Gao, 2023. "The accuracy of IMF crises nowcasts," International Journal of Forecasting, Elsevier, vol. 39(1), pages 431-449.
    16. Ines Fortin & Sebastian P. Koch & Klaus Weyerstrass, 2020. "Evaluation of economic forecasts for Austria," Empirical Economics, Springer, vol. 58(1), pages 107-137, January.
    17. Tim Köhler & Jörg Döpke, 2023. "Will the last be the first? Ranking German macroeconomic forecasters based on different criteria," Empirical Economics, Springer, vol. 64(2), pages 797-832, February.
    18. Sergey V. Smirnov, 2014. "Predicting US Recessions: Does a Wishful Bias Exist?," HSE Working papers WP BRP 77/EC/2014, National Research University Higher School of Economics.
    19. Arai, Natsuki, 2020. "Investigating the inefficiency of the CBO’s budgetary projections," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1290-1300.

    More about this item

    Keywords

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    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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