Forecasting with many predictors using message passing algorithms
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- Dimitris Korobilis, 2019. "High-dimensional macroeconomic forecasting using message passing algorithms," Working Paper series 19-17, Rimini Centre for Economic Analysis.
- Korobilis, Dimitris, 2019. "High-dimensional macroeconomic forecasting using message passing algorithms," MPRA Paper 96079, University Library of Munich, Germany.
References listed on IDEAS
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Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 596-620, June.
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- Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen V.K. Rombouts, 2015.
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Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 596-620, June.
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Citations
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Cited by:
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- Cepni, Oguzhan & Clements, Michael P., 2021. "How Local is the Local Inflation Factor? Evidence from Emerging European Countries," Working Papers 8-2021, Copenhagen Business School, Department of Economics.
- Boriss Siliverstovs & Daniel S. Wochner, 2021. "State‐dependent evaluation of predictive ability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 547-574, April.
- Gary Koop & Dimitris Korobilis, 2023.
"Bayesian Dynamic Variable Selection In High Dimensions,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1047-1074, August.
- Gary Koop & Dimitris Korobilis, 2018. "Bayesian dynamic variable selection in high dimensions," Papers 1809.03031, arXiv.org, revised May 2020.
- Korobilis, Dimitris & Koop, Gary, 2020. "Bayesian dynamic variable selection in high dimensions," MPRA Paper 100164, University Library of Munich, Germany.
- Gary Koop & Dimitris Korobilis, 2020. "Bayesian dynamic variable selection in high dimensions," Working Papers 2020_11, Business School - Economics, University of Glasgow.
- Florian Huber & Gary Koop & Michael Pfarrhofer, 2020. "Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations," Papers 2002.10274, arXiv.org.
- 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.
- Korobilis, Dimitris & Pettenuzzo, Davide, 2020.
"Machine Learning Econometrics: Bayesian algorithms and methods,"
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100165, University Library of Munich, Germany.
- Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Working Papers 130, Brandeis University, Department of Economics and International Business School.
- Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Papers 2004.11486, arXiv.org.
- Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Working Papers 2020_09, Business School - Economics, University of Glasgow.
- Boriss Siliverstovs & Daniel Wochner, 2019.
"Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data,"
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- Boriss Siliverstovs & Daniel Wochner, 2020. "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," Working Papers 2020/02, Latvijas Banka.
- Hauzenberger Niko & Huber Florian & Koop Gary, 2024.
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Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 201-225, April.
- Niko Hauzenberger & Florian Huber & Gary Koop, "undated". "Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods," Working Papers 2305, University of Strathclyde Business School, Department of Economics.
- Niko Hauzenberger & Florian Huber & Gary Koop, 2020. "Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods," Papers 2005.03906, arXiv.org, revised May 2023.
- Arefiev, Nikolay & Khabibullin, Ramis, 2018. "Bayesian identification of structural vector autoregression models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 49, pages 115-142.
- Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022.
"The role of investor sentiment in forecasting housing returns in China: A machine learning approach,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
- Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2020. "The Role of Investor Sentiment in Forecasting Housing Returns in China: A Machine Learning Approach," Working Papers 202055, University of Pretoria, Department of Economics.
- Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
- George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
- Dimitris Korobilis, 2018.
"Machine Learning Macroeconometrics: A Primer,"
Working Paper series
18-30, Rimini Centre for Economic Analysis.
- Korobilis, Dimitris, 2018. "Machine Learning Macroeconometrics A Primer," Essex Finance Centre Working Papers 22666, University of Essex, Essex Business School.
- Zheng, Tingguo & Ye, Shiqi & Hong, Yongmiao, 2023. "Fast estimation of a large TVP-VAR model with score-driven volatilities," Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
- Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2021. "General Bayesian time-varying parameter VARs for predicting government bond yields," Papers 2102.13393, arXiv.org.
- Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
- Fischer, Manfred M. & Hauzenberger, Niko & Huber, Florian & Pfarrhofer, Michael, 2022. "General Bayesian time-varying parameter VARs for modeling government bond yields," Working Papers in Regional Science 2021/01, WU Vienna University of Economics and Business.
- Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
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More about this item
Keywords
high-dimensional inference; compressive sensing; belief propagation; Bayesian shrinkage; dynamic factor models;All these keywords.
JEL classification:
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
NEP fields
This paper has been announced in the following NEP Reports:- NEP-CMP-2017-05-07 (Computational Economics)
- NEP-ECM-2017-05-07 (Econometrics)
- NEP-ETS-2017-05-07 (Econometric Time Series)
- NEP-FOR-2017-05-07 (Forecasting)
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