Robust Forecast Comparison
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- Jin, Sainan & Corradi, Valentina & Swanson, Norman R., 2017. "Robust Forecast Comparison," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1306-1351, December.
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- Francis X. Diebold & Minchul Shin, 2017.
"Assessing point forecast accuracy by stochastic error distance,"
Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 588-598, October.
- Francis X. Diebold & Minchul Shin, 2014. "Assessing Point Forecast Accuracy by Stochastic Error Distance," PIER Working Paper Archive 14-038, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Francis X. Diebold & Minchul Shin, 2016. "Assessing Point Forecast Accuracy by Stochastic Error Distance," NBER Working Papers 22516, National Bureau of Economic Research, Inc.
- Arvanitis, Stelios & Post, Thierry & Potì, Valerio & Karabati, Selcuk, 2021. "Nonparametric tests for Optimal Predictive Ability," International Journal of Forecasting, Elsevier, vol. 37(2), pages 881-898.
- Post, Thierry & Karabatı, Selçuk & Arvanitis, Stelios, 2019. "Robust optimization of forecast combinations," International Journal of Forecasting, Elsevier, vol. 35(3), pages 910-926.
- Norman R. Swanson & Weiqi Xiong, 2018.
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Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
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- Sander Barendse & Andrew J. Patton, 2020. "Comparing Predictive Accuracy in the Presence of a Loss Function Shape Parameter," Economics Series Working Papers 909, University of Oxford, Department of Economics.
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- Yen, Yu-Min & Yen, Tso-Jung, 2021. "Testing forecast accuracy of expectiles and quantiles with the extremal consistent loss functions," International Journal of Forecasting, Elsevier, vol. 37(2), pages 733-758.
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More about this item
Keywords
convex loss function; empirical processes; forecast superiority; general loss function;All these keywords.
JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2015-07-04 (Econometrics)
- NEP-ETS-2015-07-04 (Econometric Time Series)
- NEP-FOR-2015-07-04 (Forecasting)
- NEP-SEA-2015-07-04 (South East Asia)
Statistics
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