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Predicting relative forecasting performance: An empirical investigation

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Cited by:

  1. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
  2. Odendahl, Florens & Rossi, Barbara & Sekhposyan, Tatevik, 2023. "Evaluating forecast performance with state dependence," Journal of Econometrics, Elsevier, vol. 237(2).
  3. Granziera, Eleonora & Jalasjoki, Pirkka & Paloviita, Maritta, 2024. "The bias of the ECB inflation projections: A State-dependent analysis," Bank of Finland Research Discussion Papers 4/2024, Bank of Finland.
  4. Nima Nonejad, 2021. "Crude oil price point forecasts of the Norwegian GDP growth rate," Empirical Economics, Springer, vol. 61(5), pages 2913-2930, November.
  5. Barbara Rossi, 2021. "Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them," Journal of Economic Literature, American Economic Association, vol. 59(4), pages 1135-1190, December.
  6. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
  7. Dongyang Zhang & Cao Wang & Yu Dong, 2023. "How Does Firm ESG Performance Impact Financial Constraints? An Experimental Exploration of the COVID-19 Pandemic," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 35(1), pages 219-239, February.
  8. Nonejad, Nima, 2021. "Predicting equity premium by conditioning on macroeconomic variables: A prediction selection strategy using the price of crude oil," Finance Research Letters, Elsevier, vol. 41(C).
  9. Eleonora Granziera & Pirkka Jalasjoki & Maritta Paloviita, 2025. "The Bias of the ECB Inflation Projections: A State‐Dependent Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(3), pages 922-940, April.
  10. Sifat, Imtiaz & Zarei, Alireza & Hosseini, Seyedmehdi & Bouri, Elie, 2022. "Interbank liquidity risk transmission to large emerging markets in crisis periods," International Review of Financial Analysis, Elsevier, vol. 82(C).
  11. Nonejad, Nima, 2023. "Conditional out-of-sample predictability of aggregate equity returns and aggregate equity return volatility using economic variables," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 91-122.
  12. Granziera, Eleanora & Larsen, Wegard H. & Meggiorini, Greta & Melosi, Leonardo, 2025. "Speaking of Inflation : The Influence of Fed Speeches on Expectations," The Warwick Economics Research Paper Series (TWERPS) 1555, University of Warwick, Department of Economics.
  13. Nonejad, Nima, 2022. "An interesting finding about the ability of geopolitical risk to forecast aggregate equity return volatility out-of-sample," Finance Research Letters, Elsevier, vol. 47(PB).
  14. Eleonora Granziera & Vegard H. Larsen & Greta Meggiorini & Leonardo Melosi, 2025. "Speaking Of Inflation: The Influence Of Fed Speeches On Expectations," Working Papers No 07/2025, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  15. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
  16. Gibbs, Christopher G. & Vasnev, Andrey L., 2024. "Conditionally optimal weights and forward-looking approaches to combining forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1734-1751.
  17. Eleonora Granziera & Vegard H. Larsen & Greta Meggiorini & Leonardo Melosi, 2025. "Speaking of Inflation: The Influence of Fed Speeches on Expectations," CESifo Working Paper Series 11992, CESifo.
  18. Luca Barbaglia & Sergio Consoli & Sebastiano Manzan, 2024. "Forecasting GDP in Europe with textual data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 338-355, March.
  19. James Yae & Yang Luo, 2023. "Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
  20. Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).
  21. Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
  22. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024. "Predicting Bond Return Predictability," Management Science, INFORMS, vol. 70(2), pages 931-951, February.
  23. Makni, Mohammed S., 2023. "Analyzing the impact of COVID-19 on the performance of listed firms in Saudi market," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
  24. Nonejad, Nima, 2022. "Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
  25. Nonejad, Nima, 2022. "Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact," Energy Economics, Elsevier, vol. 115(C).
  26. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.
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