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Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules
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- Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
- Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2013.
"Time-varying combinations of predictive densities using nonlinear filtering,"
Journal of Econometrics, Elsevier, vol. 177(2), pages 213-232.
- Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Time-varying Combinations of Predictive Densities using Nonlinear Filtering," Tinbergen Institute Discussion Papers 12-118/III, Tinbergen Institute.
- Szendrei, Tibor & Varga, Katalin, 2023. "Revisiting vulnerable growth in the Euro Area: Identifying the role of financial conditions in the distribution," Economics Letters, Elsevier, vol. 223(C).
- Carlos DÃaz, 2018.
"Extracting information shocks from the Bank of England inflation density forecasts,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 316-326, April.
- Carlos Diaz Vela, 2016. "Extracting the Information Shocks from the Bank of England Inflation Density Forecasts," Discussion Papers in Economics 16/13, Division of Economics, School of Business, University of Leicester.
- Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022.
"Nowcasting tail risk to economic activity at a weekly frequency,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 843-866, August.
- Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2021. "Nowcasting Tail Risk to Economic Activity at a Weekly Frequency," CEPR Discussion Papers 16496, C.E.P.R. Discussion Papers.
- Jan Prüser & Florian Huber, 2024.
"Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 269-291, March.
- Jan Pruser & Florian Huber, 2023. "Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions," Papers 2301.13604, arXiv.org, revised Sep 2023.
- Huber, Florian & Onorante, Luca & Pfarrhofer, Michael, 2024.
"Forecasting euro area inflation using a huge panel of survey expectations,"
International Journal of Forecasting, Elsevier, vol. 40(3), pages 1042-1054.
- Florian Huber & Luca Onorante & Michael Pfarrhofer, 2022. "Forecasting euro area inflation using a huge panel of survey expectations," Papers 2207.12225, arXiv.org.
- 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.
- Davide Delle Monache & Ivan Petrella, 2016. "Adaptive models and heavy tails with an application to inflation forecasting," BCAM Working Papers 1603, Birkbeck Centre for Applied Macroeconomics.
- Clements, Michael P., 2018.
"Are macroeconomic density forecasts informative?,"
International Journal of Forecasting, Elsevier, vol. 34(2), pages 181-198.
- Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
- Tino Werner, 2022. "Elicitability of Instance and Object Ranking," Decision Analysis, INFORMS, vol. 19(2), pages 123-140, June.
- Pauwels, Laurent & Radchenko, Peter & Vasnev, Andrey, 2019.
"Higher Moment Constraints for Predictive Density Combinations,"
Working Papers
BAWP-2019-01, University of Sydney Business School, Discipline of Business Analytics.
- Pauwels, Laurent & Radchenko, Peter & Vasnev, Andrey, 2020. "Higher Moment Constraints for Predictive Density Combinations," Working Papers BAWP-2020-01, University of Sydney Business School, Discipline of Business Analytics.
- Emilio Zanetti Chini, 2018.
"Forecaster’s utility and forecasts coherence,"
DEM Working Papers Series
145, University of Pavia, Department of Economics and Management.
- Emilio Zanetti Chini, 2018. "Forecaster’s utility and forecasts coherence," CREATES Research Papers 2018-01, Department of Economics and Business Economics, Aarhus University.
- Emilio Zanetti Chini, 2018. "Forecasters’ utility and forecast coherence," CREATES Research Papers 2018-23, Department of Economics and Business Economics, Aarhus University.
- Carola Conces Binder & Rodrigo Sekkel, 2024.
"Central bank forecasting: A survey,"
Journal of Economic Surveys, Wiley Blackwell, vol. 38(2), pages 342-364, April.
- Carola Conces Binder & Rodrigo Sekkel, 2023. "Central Bank Forecasting: A Survey," Staff Working Papers 23-18, Bank of Canada.
- Taillardat, Maxime & Fougères, Anne-Laure & Naveau, Philippe & de Fondeville, Raphaël, 2023. "Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1448-1459.
- Bjørnland, Hilde C. & Ravazzolo, Francesco & Thorsrud, Leif Anders, 2017.
"Forecasting GDP with global components: This time is different,"
International Journal of Forecasting, Elsevier, vol. 33(1), pages 153-173.
- Hilde C. Bjørnland & Francesco Ravazzolo & Leif Anders Thorsrud, 2015. "Forecasting GDP with global components. This time is different," Working Paper 2015/05, Norges Bank.
- Hilde C. Bjørnland & Francesco Ravazzolo & Leif Anders Thorsrud, 2015. "Forecasting GDP with global components. This time is different," Working Papers No 1/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Hilde C. Bjornland & Francesco Ravazzolo & Leif Anders Thorsrud, 2016. "Forecasting GDP with global components. This time is different," CAMA Working Papers 2016-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Timmermann, Allan & Pettenuzzo, Davide & Valkanov, Rossen, 2014.
"A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics,"
CEPR Discussion Papers
10160, C.E.P.R. Discussion Papers.
- Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
- Berg, Tim O. & Henzel, Steffen R., 2015.
"Point and density forecasts for the euro area using Bayesian VARs,"
International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
- Berg, Tim Oliver & Henzel, Steffen, 2013. "Point and Density Forecasts for the Euro Area Using Many Predictors: Are Large BVARs Really Superior?," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79783, Verein für Socialpolitik / German Economic Association.
- Tim Oliver Berg & Steffen Henzel, 2013. "Point and Density Forecasts for the Euro Area Using Many Predictors: Are Large BVARs Really Superior?," ifo Working Paper Series 155, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
- Tim Oliver Berg & Steffen Henzel, 2014. "Point and Density Forecasts for the Euro Area Using Bayesian VARs," CESifo Working Paper Series 4711, CESifo.
- Chris McDonald & Craig Thamotheram & Shaun P. Vahey & Elizabeth C. Wakerly, 2016.
"Assessing the economic value of probabilistic forecasts in the presence of an inflation target,"
Reserve Bank of New Zealand Discussion Paper Series
DP2016/10, Reserve Bank of New Zealand.
- Christopher McDonald & Craig Thamotheram & Shaun P. Vahey & Elizabeth C. Wakerly, 2016. "Assessing the economic value of probabilistic forecasts in the presence of an inflation target," CAMA Working Papers 2016-40, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Luisa Bisaglia & Matteo Grigoletto, 2018. "A new time-varying model for forecasting long-memory series," Papers 1812.07295, arXiv.org.
- Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2024.
"Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions,"
Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(5), pages 1099-1127, August.
- Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
- Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.
- Knotek, Edward S. & Zaman, Saeed, 2023.
"Real-time density nowcasts of US inflation: A model combination approach,"
International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
- Edward Knotek & Saeed Zaman, 2020. "Real-time density nowcasts of US inflation: a model-combination approach," Working Papers 2015, University of Strathclyde Business School, Department of Economics.
- Edward S. Knotek & Saeed Zaman, 2020. "Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach," Working Papers 20-31, Federal Reserve Bank of Cleveland.
- Davide Pettenuzzo & Francesco Ravazzolo, 2016.
"Optimal Portfolio Choice Under Decision‐Based Model Combinations,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
- Davide Pettenuzzo & Francesco Ravazzolo, 2014. "Optimal Portfolio Choice under Decision-Based Model Combinations," Working Papers 80, Brandeis University, Department of Economics and International Business School.
- Davide Pettenuzzo & Francesco Ravazzolo, 2015. "Optimal Portfolio Choice under Decision-Based Model Combinations," Working Papers No 9/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Davide Pettenuzzo & Francesco Ravazzolo, 2014. "Optimal portfolio choice under decision-based model combinations," Working Paper 2014/15, Norges Bank.
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2024.
"Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(4), pages 1302-1317, October.
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model," Papers 2110.03411, arXiv.org.
- Todd Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model," Working Papers 2307, University of Strathclyde Business School, Department of Economics.
- Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2023. "Investigating Growth-at-Risk Using a Multicountry Non-parametric Quantile Factor Model," CEPR Discussion Papers 18549, C.E.P.R. Discussion Papers.
- Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
- Tobias Fissler & Hajo Holzmann, 2022. "Measurability of functionals and of ideal point forecasts," Papers 2203.08635, arXiv.org.
- Catania, Leopoldo & Luati, Alessandra, 2020. "Robust estimation of a location parameter with the integrated Hogg function," Statistics & Probability Letters, Elsevier, vol. 164(C).
- Ramon de Punder & Timo Dimitriadis & Rutger-Jan Lange, 2024.
"Kullback-Leibler-based characterizations of score-driven updates,"
Tinbergen Institute Discussion Papers
24-051/III, Tinbergen Institute, revised 22 Oct 2024.
- Ramon de Punder & Timo Dimitriadis & Rutger-Jan Lange, 2024. "Kullback-Leibler-based characterizations of score-driven updates," Papers 2408.02391, arXiv.org, revised Sep 2024.
- Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
- Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
- James Mitchell & Aubrey Poon & Dan Zhu, 2024.
"Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 790-812, August.
- James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
- Silius M. Vandeskog & Sara Martino & Daniela Castro-Camilo & Håvard Rue, 2022. "Modelling Sub-daily Precipitation Extremes with the Blended Generalised Extreme Value Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 598-621, December.
- Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024.
"Daily growth at risk: Financial or real drivers? The answer is not always the same,"
International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
- Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2022. ""Daily Growth at Risk: financial or real drivers? The answer is not always the same"," IREA Working Papers 202208, University of Barcelona, Research Institute of Applied Economics, revised Jun 2022.
- Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
- Moisan, Stella & Herrera, Rodrigo & Clements, Adam, 2018.
"A dynamic multiple equation approach for forecasting PM2.5 pollution in Santiago, Chile,"
International Journal of Forecasting, Elsevier, vol. 34(4), pages 566-581.
- Stella Moisan & Rodrigo Herrera & Adam Clements, 2017. "A Dynamic Multiple Equation Approach for Forecasting PM2.5 Pollution in Santiago, Chile," NCER Working Paper Series 117, National Centre for Econometric Research.
- Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023.
"Predictive Density Combination Using a Tree-Based Synthesis Function,"
Staff Working Papers
23-61, Bank of Canada.
- Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Predictive Density Combination Using a Tree-Based Synthesis Function," Working Papers 23-30, Federal Reserve Bank of Cleveland.
- Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Predictive Density Combination Using a Tree-Based Synthesis Function," Papers 2311.12671, arXiv.org.
- Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.
- Klein, Nadja & Denuit, Michel & Lang, Stefan & Kneib, Thomas, 2014. "Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 225-249.
- Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021.
"The time-varying risk of Italian GDP,"
Economic Modelling, Elsevier, vol. 101(C).
- Fabio Busetti & Michele Caivano & Davide Delle Monache & Claudia Pacella, 2020. "The time-varying risk of Italian GDP," Temi di discussione (Economic working papers) 1288, Bank of Italy, Economic Research and International Relations Area.
- Nima Nonejad, 2020. "Does the price of crude oil help predict the conditional distribution of aggregate equity return?," Empirical Economics, Springer, vol. 58(1), pages 313-349, January.
- Francesco Ravazzolo & Marco J. Lombardi, 2012.
"Oil price density forecasts: Exploring the linkages with stock markets,"
Working Papers
No 3/2012, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Marco J. Lombardi & Francesco Ravazzolo, 2012. "Oil price density forecasts: exploring the linkages with stock markets," Working Paper 2012/24, Norges Bank.
- Elie Bouri & Rangan Gupta & Luca Rossini, 2022. "The Role of the Monthly ENSO in Forecasting the Daily Baltic Dry Index," Working Papers 202229, University of Pretoria, Department of Economics.
- Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
- Emilio Zanetti Chini, 2019. "Strategic judgment: its game-theoretic foundations,its econometric elicitation," Working Papers in Public Economics 190, Department of Economics and Law, Sapienza University of Roma.
- David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin, 2021.
"Variational Bayes in State Space Models: Inferential and Predictive Accuracy,"
Papers
2106.12262, arXiv.org, revised Feb 2022.
- David T. Frazier & Gael M. Martin & Ruben Loaiza-Maya, 2022. "Variational Bayes in State Space Models: Inferential and Predictive Accuracy," Monash Econometrics and Business Statistics Working Papers 1/22, Monash University, Department of Econometrics and Business Statistics.
- Onno Kleen, 2024. "Scaling and measurement error sensitivity of scoring rules for distribution forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 833-849, August.
- Alvaro Arroyo & Alvaro Cartea & Fernando Moreno-Pino & Stefan Zohren, 2023. "Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers," Papers 2306.05479, arXiv.org.
- David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
- Ruben Loaiza‐Maya & Gael M. Martin & David T. Frazier, 2021.
"Focused Bayesian prediction,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 517-543, August.
- Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier, 2019. "Focused Bayesian Prediction," Papers 1912.12571, arXiv.org, revised Aug 2020.
- Ruben Loaiza-Maya & Gael M Martin & David T. Frazier, 2020. "Focused Bayesian Prediction," Monash Econometrics and Business Statistics Working Papers 1/20, Monash University, Department of Econometrics and Business Statistics.
- Ferrara, Laurent & Mogliani, Matteo & Sahuc, Jean-Guillaume, 2022.
"High-frequency monitoring of growth at risk,"
International Journal of Forecasting, Elsevier, vol. 38(2), pages 582-595.
- Laurent Ferrara & Matteo Mogliani & Jean-Guillaume Sahuc, 2020. "High-frequency monitoring of growth-at-risk," CAMA Working Papers 2020-97, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Jean-Guillaume Sahuc & Matteo Mogliani & Laurent Ferrara, 2022. "High-frequency monitoring of growth at risk," Post-Print hal-03361425, HAL.
- Gensler, André & Sick, Bernhard & Vogt, Stephan, 2018. "A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 352-379.
- Benjamin Avanzi & Yanfeng Li & Bernard Wong & Alan Xian, 2022. "Ensemble distributional forecasting for insurance loss reserving," Papers 2206.08541, arXiv.org, revised Jun 2024.
- Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2023.
"Are low frequency macroeconomic variables important for high frequency electricity prices?,"
Economic Modelling, Elsevier, vol. 120(C).
- Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers 2007.13566, arXiv.org, revised Dec 2022.
- repec:wrk:wrkemf:09 is not listed on IDEAS
- Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020.
"Proper scoring rules for evaluating asymmetry in density forecasting,"
Papers
2006.11265, arXiv.org, revised Sep 2020.
- Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Working Papers No 06/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017.
"Tracking the Slowdown in Long-Run GDP Growth,"
The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
- Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2014. "Tracking the Slowdown in Long-Run GDP Growth," Discussion Papers 1604, Centre for Macroeconomics (CFM), revised Jan 2016.
- Antolin-Diaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2016. "Tracking the slowdown in long-run GDP growth," Bank of England working papers 587, Bank of England.
- Antolin-Diaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2017. "Tracking the slowdown in long-run GDP growth," LSE Research Online Documents on Economics 81869, London School of Economics and Political Science, LSE Library.
- Antolin-Diaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2016. "Tracking the slowdown in long-run GDP growth," LSE Research Online Documents on Economics 86243, London School of Economics and Political Science, LSE Library.
- Tamás Kiss & Stepan Mazur & Hoang Nguyen & Pär Österholm, 2023.
"Modeling the relation between the US real economy and the corporate bond‐yield spread in Bayesian VARs with non‐Gaussian innovations,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 347-368, March.
- Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang & Österholm, Pär, 2021. "Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances," Working Papers 2021:9, Örebro University, School of Business.
- Andrew Harvey & Ryoko Ito, 2017. "Modeling time series with zero observations," Economics Papers 2017-W01, Economics Group, Nuffield College, University of Oxford.
- Lee Tae-Hwy & Wang He & Xi Zhou & Zhang Ru, 2023.
"Density Forecast of Financial Returns Using Decomposition and Maximum Entropy,"
Journal of Econometric Methods, De Gruyter, vol. 12(1), pages 57-83, January.
- Tae-Hwy Lee & He Wang & Zhou Xi & Ru Zhang, 2021. "Density Forecast of Financial Returns Using Decomposition and Maximum Entropy," Working Papers 202115, University of California at Riverside, Department of Economics.
- Nonejad, Nima, 2022. "Forecasting crude oil price volatility out-of-sample using news-based geopolitical risk index: What forms of nonlinearity help improve forecast accuracy the most?," Finance Research Letters, Elsevier, vol. 46(PA).
- Hajo Holzmann & Matthias Eulert, 2014. "The role of the information set for forecasting - with applications to risk management," Papers 1404.7653, arXiv.org.
- Klein, Nadja & Denuit, Michel & Lang, Stefan & Kneib, Thomas, 2013. "Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape," LIDAM Discussion Papers ISBA 2013045, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022.
"Specification Choices in Quantile Regression for Empirical Macroeconomics,"
Working Papers
22-25, Federal Reserve Bank of Cleveland.
- Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2024. "Specification Choices in Quantile Regression for Empirical Macroeconomics," CEPR Discussion Papers 18901, C.E.P.R. Discussion Papers.
- Alexander, Carol & Kaeck, Andreas & Sumawong, Anannit, 2019. "A parsimonious parametric model for generating margin requirements for futures," European Journal of Operational Research, Elsevier, vol. 273(1), pages 31-43.
- Nima Nonejad, 2021. "Crude oil price point forecasts of the Norwegian GDP growth rate," Empirical Economics, Springer, vol. 61(5), pages 2913-2930, November.
- Andrea Bastianin & Elisabetta Mirto & Yan Qin & Luca Rossini, 2024.
"What drives the European carbon market? Macroeconomic factors and forecasts,"
Papers
2402.04828, arXiv.org, revised Feb 2024.
- Andrea Bastianin & Elisabetta Mirto & Yan Qin & Luca Rossini, 2024. "What drives the European carbon market? Macroeconomic factors and forecasts," Working Papers 2024.02, Fondazione Eni Enrico Mattei.
- Bastianin, Andrea & Mirto, Elisabetta & Qin, Yan & Rossini, Luca, 2024. "What drives the European carbon market? Macroeconomic factors and forecasts," FEEM Working Papers 339740, Fondazione Eni Enrico Mattei (FEEM).
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023.
"Tail Forecasting With Multivariate Bayesian Additive Regression Trees,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," Working Papers 21-08R, Federal Reserve Bank of Cleveland, revised 12 Jul 2022.
- Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
- C. Alexander & M. Coulon & Y. Han & X. Meng, 2024.
"Evaluating the discrimination ability of proper multi-variate scoring rules,"
Annals of Operations Research, Springer, vol. 334(1), pages 857-883, March.
- Carol Alexander & Michael Coulon & Yang Han & Xiaochun Meng, 2021. "Evaluating the Discrimination Ability of Proper Multivariate Scoring Rules," Papers 2101.12693, arXiv.org.
- Michael S. Smith & Shaun P. Vahey, 2016. "Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 416-434, July.
- Konrad Bogner & Florian Pappenberger & Massimiliano Zappa, 2019. "Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts," Sustainability, MDPI, vol. 11(12), pages 1-22, June.
- Catania, Leopoldo & Grassi, Stefano, 2022. "Forecasting cryptocurrency volatility," International Journal of Forecasting, Elsevier, vol. 38(3), pages 878-894.
- Manzan, Sebastiano & Zerom, Dawit, 2013.
"Are macroeconomic variables useful for forecasting the distribution of U.S. inflation?,"
International Journal of Forecasting, Elsevier, vol. 29(3), pages 469-478.
- Manzan, Sebastiano & Zerom, Dawit, 2009. "Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?," MPRA Paper 14387, University Library of Munich, Germany.
- Berk, K. & Hoffmann, A. & Müller, A., 2018. "Probabilistic forecasting of industrial electricity load with regime switching behavior," International Journal of Forecasting, Elsevier, vol. 34(2), pages 147-162.
- Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2021. "Forecasting natural gas prices using highly flexible time-varying parameter models," Economic Modelling, Elsevier, vol. 105(C).
- Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2024. "A Quantile Nelson-Siegel model," Papers 2401.09874, arXiv.org.
- Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023.
"Forecasting electricity prices with expert, linear, and nonlinear models,"
International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
- Anna Gloria Billé & Angelica Gianfreda & Filippo Del Grosso & Francesco Ravazzolo, 2021. "Forecasting Electricity Prices with Expert, Linear and Non-Linear Models," Working Paper series 21-20, Rimini Centre for Economic Analysis.
- Iacopini, Matteo & Poon, Aubrey & Rossini, Luca & Zhu, Dan, 2023.
"Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP,"
Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
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- Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020.
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