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Forecast Evaluation Under Asymmetric Loss: A Monte Carlo Analysis of the EKT Method

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  • Jens J. Krüger
  • Julian LeCrone

Abstract

This paper contributes to the literature on forecast evaluation by conducting an extensive Monte Carlo experiment using the evaluation procedure proposed by Elliott, Komunjer and Timmermann. We consider recent developments in weighting matrices for GMM estimation and testing. We pay special attention to the size and power properties of variants of the J‐test of forecast rationality. Proceeding from a baseline scenario to a more realistic setting, our results show that the approach leads to precise estimates of the degree of asymmetry of the loss function. For correctly specified models, we find the size of the J‐tests to be close to the nominal size, while the tests have high power against misspecified models. These findings are quite robust to inducing fat tails, serial correlation and outliers.

Suggested Citation

  • Jens J. Krüger & Julian LeCrone, 2019. "Forecast Evaluation Under Asymmetric Loss: A Monte Carlo Analysis of the EKT Method," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(2), pages 437-455, April.
    • Handle: RePEc:bla:obuest:v:81:y:2019:i:2:p:437-455
    DOI: 10.1111/obes.12268
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    Cited by:

    1. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    2. 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.
    3. 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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