Adaptive models and heavy tails with an application to inflation forecasting
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- Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
- Delle Monache, Davide & Petrella, Ivan, 2016. "Adaptive models and heavy tails with an application to inflation forecasting," MPRA Paper 75424, University Library of Munich, Germany.
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Citations
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- 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.
- Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2021. "Modeling and forecasting macroeconomic downside risk," Temi di discussione (Economic working papers) 1324, Bank of Italy, Economic Research and International Relations Area.
- Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2022. "Modeling and Forecasting Macroeconomic Downside Risk," CEPR Discussion Papers 15109, C.E.P.R. Discussion Papers.
- 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.
- 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.
- Delle Monache, Davide & Petrella, Ivan & Venditti, Fabrizio, 2019. "Price Dividend Ratio and Long-Run Stock Returns: a Score Driven State Space Model," EMF Research Papers 29, Economic Modelling and Forecasting Group.
- Petrella, Ivan & Delle Monache, Davide & Venditti, Fabrizio, 2019. "Price Dividend Ratio and Long-Run Stock Returns: a Score Driven State Space Model," CEPR Discussion Papers 14107, C.E.P.R. Discussion Papers.
- Delle Monache, Davide & Petrella, Ivan & Venditti, Fabrizio, 2020. "Price dividend ratio and long-run stock returns: a score driven state space model," Temi di discussione (Economic working papers) 1296, Bank of Italy, Economic Research and International Relations Area.
- Delle Monache, Davide & Venditti, Fabrizio & Petrella, Ivan, 2020. "Price dividend ratio and long-run stock returns: a score driven state space model," Working Paper Series 2369, European Central Bank.
- Barbara Rossi, 2019.
"Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them,"
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- Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
- Rossi, Barbara, 2020. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," CEPR Discussion Papers 14472, C.E.P.R. Discussion Papers.
- 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.
- Mogens Fosgerau & Jinwon Kim & Abhishek Ranjan, 2017. "Vickrey Meets Alonso: Commute Scheduling and Congestion in a Monocentric City," Discussion Papers 17-25, University of Copenhagen. Department of Economics.
- Henrik Jensen & Ivan Petrella & Søren Hove Ravn & Emiliano Santoro, 2017. "Leverage and deepening business cycle skewness," Working Papers 1732, Banco de España.
- Jensen, Henrik & Petrella, Ivan & Ravn, Soren & Santoro, Emiliano, 2019. "Leverage and Deepening Business Cycle Skewness," EMF Research Papers 21, Economic Modelling and Forecasting Group.
- Petrella, Ivan & Jensen, Henrik & Ravn, Søren Hove & Santoro, Emiliano, 2017. "Leverage and Deepening Business Cycle Skewness," CEPR Discussion Papers 12239, C.E.P.R. Discussion Papers.
- 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.
- Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
- 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.
- 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.
- 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.
- Delle-Monache, Davide & De-Polis, Andrea & Petrella, Ivan, 2020. "Modelling and Forecasting Macroeconomic Downside Risk," EMF Research Papers 34, Economic Modelling and Forecasting Group.
- Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2021. "Modeling and forecasting macroeconomic downside risk," Temi di discussione (Economic working papers) 1324, Bank of Italy, Economic Research and International Relations Area.
- Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2022. "Modeling and Forecasting Macroeconomic Downside Risk," CEPR Discussion Papers 15109, C.E.P.R. Discussion Papers.
- Blasques, F. & Gorgi, P. & Koopman, S.J., 2019. "Accelerating score-driven time series models," Journal of Econometrics, Elsevier, vol. 212(2), pages 359-376.
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"Forecasting economic time series using score-driven dynamic models with mixed-data sampling,"
International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
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- 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.
- Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
- Martin Weale & Paul Labonne, 2022. "Nowcasting in the presence of large measurement errors and revisions," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-05, Economic Statistics Centre of Excellence (ESCoE).
- Harvey, A., 2021. "Score-driven time series models," Cambridge Working Papers in Economics 2133, Faculty of Economics, University of Cambridge.
- 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.
- 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.
- 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.
- Paul Labonne, 2020. "Asymmetric uncertainty : Nowcasting using skewness in real-time data," Papers 2012.02601, arXiv.org, revised May 2024.
- 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.
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"Adaptive models and heavy tails with an application to inflation forecasting,"
International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
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More about this item
Keywords
adaptive algorithms; inflation; score-driven models; student-t; time-varying parameters.;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2016-12-18 (Econometrics)
- NEP-ETS-2016-12-18 (Econometric Time Series)
- NEP-FOR-2016-12-18 (Forecasting)
- NEP-MAC-2016-12-18 (Macroeconomics)
Statistics
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