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Mincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions

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  • Kemal Guler
  • Pin T. Ng
  • Zhijie Xiao

Abstract

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  • Kemal Guler & Pin T. Ng & Zhijie Xiao, 2017. "Mincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(6), pages 651-679, September.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:6:p:651-679
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    Cited by:

    1. Malte Knuppel & Fabian Kruger & Marc-Oliver Pohle, 2022. "Score-based calibration testing for multivariate forecast distributions," Papers 2211.16362, arXiv.org, revised Dec 2023.
    2. 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.
    3. Sebastian Bayer & Timo Dimitriadis, 2018. "Regression Based Expected Shortfall Backtesting," Papers 1801.04112, arXiv.org, revised Sep 2019.
    4. Sebastian Bayer & Timo Dimitriadis, 2022. "Regression-Based Expected Shortfall Backtesting [Backtesting Expected Shortfall]," Journal of Financial Econometrics, Oxford University Press, vol. 20(3), pages 437-471.
    5. 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.

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