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Adaptive models and heavy tails with an application to inflation forecasting

Citations

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Cited by:

  1. Hilde C. Bjørnland & Roberto Casarin & Marco Lorusso & Francesco Ravazzolo, 2026. "Fiscal Policy Regimes in Resource‐Rich Economies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 41(6), pages 776-790, September.
  2. Davide Delle Monache & Ivan Petrella & Fabrizio Venditti, 2021. "Price Dividend Ratio and Long-Run Stock Returns: A Score-Driven State Space Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1054-1065, October.
  3. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.
  4. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
  5. Henrik Jensen & Ivan Petrella & Søren Hove Ravn & Emiliano Santoro, 2020. "Leverage and Deepening Business-Cycle Skewness," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(1), pages 245-281, January.
  6. Luisa Bisaglia & Matteo Grigoletto, 2021. "A new time-varying model for forecasting long-memory series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 139-155, March.
  7. Blasques, F. & Gorgi, P. & Koopman, S.J., 2019. "Accelerating score-driven time series models," Journal of Econometrics, Elsevier, vol. 212(2), pages 359-376.
  8. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
  9. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
  10. Harvey, A., 2021. "Score-driven time series models," Cambridge Working Papers in Economics 2133, Faculty of Economics, University of Cambridge.
  11. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.
  12. Krist'of N'emeth & D'aniel Hadh'azi, 2024. "Generating density nowcasts for U.S. GDP growth with deep learning: Bayes by Backprop and Monte Carlo dropout," Papers 2405.15579, arXiv.org.
  13. 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.
  14. repec:nsr:escoed:escoe-dp-2022-23 is not listed on IDEAS
  15. Labonne, Paul, 2025. "Asymmetric uncertainty: Nowcasting using skewness in real-time data," International Journal of Forecasting, Elsevier, vol. 41(1), pages 229-250.
  16. De Polis, Andrea & Melosi, Leonardo & Petrella, Ivan, 2024. "The Taming of the Skew: Asymmetric Inflation Risk and Monetary Policy," CEPR Discussion Papers 19760, Centre for Economic Policy Research.
  17. Tretyakov, Dmitriy & Fokin, Nikita, 2020. "Помогают Ли Высокочастотные Данные В Прогнозировании Российской Инфляции? [Does the high-frequency data is helpful for forecasting Russian inflation?]," MPRA Paper 109556, University Library of Munich, Germany.
  18. Davide Delle Monache & Andrea De Polis & Ivan Petrella, 2024. "Modeling and Forecasting Macroeconomic Downside Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 1010-1025, July.
  19. repec:nsr:escoed:escoe-dp-2022-05 is not listed on IDEAS
  20. Rui Zhang & Xiaogang Dong, 2025. "Statistical inference for the binomial autoregressive model with time-varying parameters," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 88(6), pages 1395-1423, August.
  21. Rui Zhang & Xiaogang Dong, 2026. "Statistical inference for the binomial autoregressive model with time-varying parameters," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 89(1), pages 29-57, January.
  22. F Blasques & P Gorgi & S J Koopman & O Wintenberger, 2016. "Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models ," Working Papers hal-01377971, HAL.
  23. Labonne, Paul & Thorsrud, Leif Anders, 2026. "Risky news and credit market sentiment," Journal of Economic Dynamics and Control, Elsevier, vol. 187(C).
  24. Roberto Duncan & Enrique Martínez‐García, 2023. "Forecasting inflation in open economies: What can a NOEM model do?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 481-513, April.
  25. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
  26. Paul Labonne, 2020. "Asymmetric uncertainty : Nowcasting using skewness in real-time data," Papers 2012.02601, arXiv.org, revised May 2024.
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