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Asymmetric Loss in the Greenbook and the Survey of Professional Forecasters

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  • Tae-Hwy Lee

    (Department of Economics, University of California Riverside)

  • Yiyao Wang

    (Booth School of Business, the University of Chicago)

Abstract

This paper examines forecast rationality of the Greenbook and the Survey of Professional Forecasters (SPF) under asymmetric loss functions, using the method proposed by Elliott, Komunjer and Timmermann (2005) with a rolling window strategy. Over rolling periods, the degree and direction of asymmetry in forecast loss function are time-varying. While rationality under symmetric loss is often rejected, forecast rationality under asymmetric loss is not rejected over nearly all rolling periods. Besides, real output growth is consistently under-predicted in 1990s and inflation rate is consistently over-predicted in 1980s and 1990s. Generally, inflation forecast, especially for long horizon, exhibits greater level of loss asymmetry in magnitude and frequency. The loss asymmetry of real output growth forecast is more pronounced when the last revised vintage data is used rather than real-time vintage is used. All of these results similarly hold in Greenbook and SPF. The results are also similar with different sets of instrumental variables for estimation of the asymmetric loss and for forecast rationality test.

Suggested Citation

  • Tae-Hwy Lee & Yiyao Wang, 2014. "Asymmetric Loss in the Greenbook and the Survey of Professional Forecasters," Working Papers 201407, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:201407
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    Cited by:

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    2. Sun, Yuying & Wang, Shouyang & Zhang, Xun, 2018. "How efficient are China's macroeconomic forecasts? Evidences from a new forecasting evaluation approach," Economic Modelling, Elsevier, vol. 68(C), pages 506-513.
    3. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    4. Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019. "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, vol. 35(1), pages 80-99.
    5. Tsuchiya, Yoichi, 2015. "Herding behavior and loss functions of exchange rate forecasters over interventions and financial crises," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 266-276.
    6. 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.
    7. Tae-Hwy Lee & Tao Wang, 2023. "Estimation and Testing of Forecast Rationality with Many Moments," Papers 2309.09481, arXiv.org.
    8. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    9. Viola Monostoriné Grolmusz, 2023. "Optimal Forecast Combination Under Asymmetric Loss and Regime-Switching," MNB Working Papers 2023/3, Magyar Nemzeti Bank (Central Bank of Hungary).
    10. Döpke Jörg & Fritsche Ulrich & Waldhof Gabi, 2019. "Theories, Techniques and the Formation of German Business Cycle Forecasts : Evidence from a survey of professional forecasters," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(2), pages 203-241, April.
    11. Jörg Döpke & Ulrich Fritsche & Karsten Müller, 2018. "Has Macroeconomic Forecasting changed after the Great Recession? - Panel-based Evidence on Accuracy and Forecaster Behaviour from Germany," Macroeconomics and Finance Series 201803, University of Hamburg, Department of Socioeconomics.
    12. Yoichi Tsuchiya, 2022. "Evaluating plant managers’ production plans over business cycles: asymmetric loss and rationality," SN Business & Economics, Springer, vol. 2(8), pages 1-29, August.
    13. Döpke Jörg & Fritsche Ulrich & Waldhof Gabi, 2019. "Theories, Techniques and the Formation of German Business Cycle Forecasts : Evidence from a survey of professional forecasters," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(2), pages 203-241, April.
    14. Huh, Sungjun & Kim, Insu, 2020. "Growth forecast revisions over business cycles: Evidence from the Survey of Professional Forecasters," Economics Letters, Elsevier, vol. 196(C).
    15. Tsuchiya, Yoichi, 2016. "Asymmetric loss and rationality of Chinese renminbi forecasts: An implication for the trade between China and the US," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 116-127.
    16. Behrens, Christoph, 2020. "German trade forecasts since 1970: An evaluation using the panel dimension," Working Papers 26, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    17. Giovannelli, Alessandro & Pericoli, Filippo Maria, 2020. "Are GDP forecasts optimal? Evidence on European countries," International Journal of Forecasting, Elsevier, vol. 36(3), pages 963-973.
    18. Víctor López-Pérez, 2017. "Do professional forecasters behave as if they believed in the New Keynesian Phillips Curve for the euro area?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 147-174, February.

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    More about this item

    Keywords

    Greenbook; SPF; Asymmetric loss; Forecast rationality; Real output growth forecast; Inflation rate forecast; Real time data; Revised data.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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