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Dimitris Korobilis

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Business School.

    Mentioned in:

    1. > Econometrics > Time Series Models > VAR Models > Bayesian Vector autoregressions (BVARs)
    2. > Econometrics > Big Data
  2. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 2016_09, Business School - Economics, University of Glasgow.

    Mentioned in:

    1. > Econometrics > Time Series Models > VAR Models > Bayesian Vector autoregressions (BVARs)
    2. > Econometrics > Big Data

Working papers

  1. Dimitris Korobilis, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," Papers 2206.06892, arXiv.org.

    Cited by:

    1. Dimitris Korobilis & Maximilian Schröder, 2023. "Monitoring multicountry macroeconomic risk," Working Papers No 06/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. Ciccarelli, Matteo & Darracq Pariès, Matthieu & Priftis, Romanos & Angelini, Elena & Bańbura, Marta & Bokan, Nikola & Fagan, Gabriel & Gumiel, José Emilio & Kornprobst, Antoine & Lalik, Magdalena & Mo, 2024. "ECB macroeconometric models for forecasting and policy analysis," Occasional Paper Series 344, European Central Bank.
    3. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2023. "What drives core inflation? The role of supply shocks," Working Paper Series 2875, European Central Bank.
    4. Josué Diwambuena & Francesco Ravazzolo, 2022. "What are the drivers of Labor Productivity?," BEMPS - Bozen Economics & Management Paper Series BEMPS86, Faculty of Economics and Management at the Free University of Bozen.
    5. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Bayesian Modeling of Time-Varying Parameters Using Regression Trees," Working Papers 23-05, Federal Reserve Bank of Cleveland.
    6. Hilde C. Bjørnland & Malin C. Jensen & Leif Anders Thorsrud, 2023. "Business Cycle and Health Dynamics during the COVID-19 Pandemic. A Scandinavian Perspective," Working Papers No 15/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    7. Luca Gambetti & Dimitris Korobilis & John D. Tsoukalas & Francesco Zanetti, 2023. "Agreed and Disagreed Uncertainty," BCAM Working Papers 2206, Birkbeck Centre for Applied Macroeconomics.
    8. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2024. "Shocked to the core: a new model to understand euro area inflation," Research Bulletin, European Central Bank, vol. 117.

  2. Dimitris Korobilis & Maximilian Schroder, 2022. "Probabilistic quantile factor analysis," Papers 2212.10301, arXiv.org, revised Dec 2022.

    Cited by:

    1. Dimitris Korobilis & Maximilian Schröder, 2023. "Monitoring multicountry macroeconomic risk," Working Papers No 06/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. Luca Gambetti & Dimitris Korobilis & John D. Tsoukalas & Francesco Zanetti, 2023. "Agreed and Disagreed Uncertainty," BCAM Working Papers 2206, Birkbeck Centre for Applied Macroeconomics.

  3. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Papers 2112.11751, arXiv.org.

    Cited by:

    1. Dimitris Korobilis & Maximilian Schröder, 2023. "Monitoring multicountry macroeconomic risk," Working Papers No 06/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. 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.
    3. Francesco Ravazzolo & Luca Rossini, 2023. "Is the Price Cap for Gas Useful? Evidence from European Countries," Working Papers 2023.23, Fondazione Eni Enrico Mattei.
    4. Donald J. Lacombe & Nasima Khatun, 2023. "What are the determinants of financial well‐being? A Bayesian LASSO approach," American Journal of Economics and Sociology, Wiley Blackwell, vol. 82(1), pages 43-59, January.

  4. Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021. "The time-varying evolution of inflation risks," Working Paper Series 2600, European Central Bank.

    Cited by:

    1. policy, Work stream on macroprudential & Albertazzi, Ugo & Martin, Alberto & Assouan, Emmanuelle & Tristani, Oreste & Galati, Gabriele & Vlassopoulos, Thomas, 2021. "The role of financial stability considerations in monetary policy and the interaction with macroprudential policy in the euro area," Occasional Paper Series 272, European Central Bank.
    2. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org.
    3. 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.
    4. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    5. Yoshibumi Makabe & Yoshihiko Norimasa, 2022. "The Term Structure of Inflation at Risk: A Panel Quantile Regression Approach," Bank of Japan Working Paper Series 22-E-4, Bank of Japan.
    6. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    7. Holm-Hadulla, Fédéric & Musso, Alberto & Rodriguez-Palenzuela, Diego & Vlassopoulos, Thomas, 2021. "Evolution of the ECB’s analytical framework," Occasional Paper Series 277, European Central Bank.
    8. 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.

  5. Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Papers 2004.11486, arXiv.org.

    Cited by:

    1. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.

  6. Dimitris Korobilis, 2020. "High-dimensional macroeconomic forecasting using message passing algorithms," Papers 2004.11485, arXiv.org.

    Cited by:

    1. 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.
    2. 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.
    3. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    4. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    5. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    6. 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).
    7. 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.
    8. Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Working Papers 130, Brandeis University, Department of Economics and International Business School.
    9. 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.
    10. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    11. Boriss Siliverstovs & Daniel Wochner, 2019. "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," KOF Working papers 19-463, KOF Swiss Economic Institute, ETH Zurich.
    12. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2020. "Bayesian Modelling of TVP-VARs Using Regression Trees," Working Papers 2308, University of Strathclyde Business School, Department of Economics, revised Aug 2023.
    13. 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.
    14. 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.
    15. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Bayesian Modeling of Time-Varying Parameters Using Regression Trees," Working Papers 23-05, Federal Reserve Bank of Cleveland.
    16. 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.
    17. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    18. 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.
    19. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    20. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
    21. 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.
    22. 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.

  7. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2020. "Energy Markets and Global Economic Conditions," CESifo Working Paper Series 8282, CESifo.

    Cited by:

    1. Afees A. Salisu & Rangan Gupta & Elie Bouri, 2022. "Testing the Forecasting Power of Global Economic Conditions for the Volatility of International REITs using a GARCH-MIDAS Approach," Working Papers 202211, University of Pretoria, Department of Economics.
    2. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    3. Yoosoon Chang & Ana Maria Herrera & Elena Pesavento, 2023. "Oil Prices Uncertainty, Endogenous Regime Switching, and Inflation Anchoring," CAEPR Working Papers 2023-002 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    4. Baffes, John & Kabundi, Alain, 2023. "Commodity price shocks: Order within chaos?," Resources Policy, Elsevier, vol. 83(C).
    5. Ellwanger, Reinhard & Snudden, Stephen, 2023. "Forecasts of the real price of oil revisited: Do they beat the random walk?," Journal of Banking & Finance, Elsevier, vol. 154(C).
    6. Jacks, David & Stuermer, Martin, 2020. "Dry Bulk Shipping and the Evolution of Maritime Transport Costs, 1850-2020," MPRA Paper 104710, University Library of Munich, Germany.
    7. Delle Chiaie, Simona & Ferrara, Laurent & Giannone, Domenico, 2018. "Common factors of commodity prices," Research Bulletin, European Central Bank, vol. 51.
    8. Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022. "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, vol. 108(C).
    9. 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.
    10. Benk, Szilárd & Gillman, Max, 2023. "Identifying money and inflation expectation shocks on real oil prices," Bank of Finland Research Discussion Papers 10/2023, Bank of Finland.
    11. Kyriaki-Argyro Tsioptsia & Eleni Zafeiriou & Dimitrios Niklis & Nikolaos Sariannidis & Constantin Zopounidis, 2022. "The Corporate Economic Performance of Environmentally Eligible Firms Nexus Climate Change: An Empirical Research in a Bayesian VAR Framework," Energies, MDPI, vol. 15(19), pages 1-16, October.
    12. Valenti, Daniele & Bastianin, Andrea & Manera, Matteo, "undated". "A weekly structural VAR model of the US crude oil market," FEEM Working Papers 324040, Fondazione Eni Enrico Mattei (FEEM).
    13. Zouhaier Dhifaoui & Sami Ben Jabeur & Rabeh Khalfaoui & Muhammad Ali Nasir, 2023. "Time‐varying partial‐directed coherence approach to forecast global energy prices with stochastic volatility model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2292-2306, December.
    14. Joshua C. C. Chan, 2022. "Comparing Stochastic Volatility Specifications for Large Bayesian VARs," Papers 2208.13255, arXiv.org.
    15. Salisu, Afees A. & Gupta, Rangan & Bouri, Elie & Ji, Qiang, 2020. "The role of global economic conditions in forecasting gold market volatility: Evidence from a GARCH-MIDAS approach," Research in International Business and Finance, Elsevier, vol. 54(C).
    16. Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2019. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," Working Papers No 11/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    17. Hilde C. Bjørnland, 2022. "The effect of rising energy prices amid geopolitical developments and supply disruptions," Working Papers No 07/2022, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    18. Wang, Fangzhi & Liao, Hua, 2022. "Unexpected economic growth and oil price shocks," Energy Economics, Elsevier, vol. 116(C).
    19. Chiara Casoli & Riccardo (Jack) Lucchetti, 2022. "Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices [Commodity-price comovement and global economic activity]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 494-514.
    20. Afees A. Salisu & Philip C. Omoke & Abdulsalam Abidemi Sikiru, 2023. "Geopolitical risk and global financial cycle: Some forecasting experiments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 3-16, January.
    21. Baumeister, Christiane & Guérin, Pierre, 2021. "A comparison of monthly global indicators for forecasting growth," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1276-1295.
    22. Ellington, Michael, 2022. "Fat tails, serial dependence, and implied volatility index connections," European Journal of Operational Research, Elsevier, vol. 299(2), pages 768-779.
    23. Ali, Sara & Badshah, Ihsan & Demirer, Riza, 2023. "Anti-herding by hedge funds and its implications for expected returns," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 31-48.
    24. Guo, Yangli & Ma, Feng & Li, Haibo & Lai, Xiaodong, 2022. "Oil price volatility predictability based on global economic conditions," International Review of Financial Analysis, Elsevier, vol. 82(C).
    25. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
    26. Knut Are Aastveit & Jamie L. Cross & Herman K. van Dijk, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Working Paper 2021/3, Norges Bank.
    27. Arango-Castillo, Lenin & Orraca, María José & Molina, G. Stefano, 2023. "The global component of headline and core inflation in emerging market economies and its ability to improve forecasting performance," Economic Modelling, Elsevier, vol. 120(C).
    28. William Barcelona & Danilo Cascaldi-Garcia & Jasper Hoek & Eva Van Leemput, 2022. "What Happens in China Does Not Stay in China," International Finance Discussion Papers 1360, Board of Governors of the Federal Reserve System (U.S.).
    29. Chen, Chun-Da & Demirer, Rıza, 2022. "Oil beta uncertainty and global stock returns," Energy Economics, Elsevier, vol. 112(C).
    30. Yang, Yang & Zhang, Jiqiang & Chen, Sanpan, 2023. "Information effects of monetary policy announcements on oil price," Journal of Commodity Markets, Elsevier, vol. 30(C).
    31. Andrea Bastianin & Chiara Casoli & Marzio Galeotti, 2023. "The connectedness of Energy Transition Metals," Working Papers 2023.11, Fondazione Eni Enrico Mattei.
    32. Maryam Movahedifar & Hossein Hassani & Masoud Yarmohammadi & Mahdi Kalantari & Rangan Gupta, 2021. "A robust approach for outlier imputation: Singular Spectrum Decomposition," Working Papers 202164, University of Pretoria, Department of Economics.
    33. 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).
    34. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2023. "What drives core inflation? The role of supply shocks," Working Paper Series 2875, European Central Bank.
    35. Baumeister, Christiane & Leiva-León, Danilo & Sims, Eric, 2021. "Tracking Weekly State-Level Economic Conditions," CEPR Discussion Papers 16317, C.E.P.R. Discussion Papers.
    36. Gupta, Rangan & Sheng, Xin & Pierdzioch, Christian & Ji, Qiang, 2021. "Disaggregated oil shocks and stock-market tail risks: Evidence from a panel of 48 economics," Research in International Business and Finance, Elsevier, vol. 58(C).
    37. Charles W. Calomiris & Nida Çakır Melek & Harry Mamaysky, 2021. "Predicting the Oil Market," NBER Working Papers 29379, National Bureau of Economic Research, Inc.
    38. Lv, Wendai & Wu, Qian, 2022. "Global economic conditions index and oil price predictability," Finance Research Letters, Elsevier, vol. 48(C).
    39. Giovanni Caggiano & Efrem Castelnuovo, 2023. "Global financial uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 432-449, April.
    40. Kassouri, Yacouba, 2022. "Boom-bust cycles in oil consumption: The role of explosive bubbles and asymmetric adjustments," Energy Economics, Elsevier, vol. 111(C).
    41. Francesco Ravazzolo & Luca Rossini, 2023. "Is the Price Cap for Gas Useful? Evidence from European Countries," Working Papers 2023.23, Fondazione Eni Enrico Mattei.
    42. Yang, Tianle & Dong, Qingyuan & Du, Min & Du, Qunyang, 2023. "Geopolitical risks, oil price shocks and inflation: Evidence from a TVP–SV–VAR approach," Energy Economics, Elsevier, vol. 127(PB).
    43. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions," Working Papers 202051, University of Pretoria, Department of Economics.
    44. Zhang, Lixia & Bai, Jiancheng & Zhang, Yueyan & Cui, Can, 2023. "Global economic uncertainty and the Chinese stock market: Assessing the impacts of global indicators," Research in International Business and Finance, Elsevier, vol. 65(C).
    45. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
    46. Arabinda Basistha & Richard Startz, 2023. "Measuring Persistent Global Economic Factors with Output, Commodity Price, and Commodity Currency Data," Working Papers 23-05, Department of Economics, West Virginia University.
    47. Wang, Jiqian & Ma, Feng & Bouri, Elie & Zhong, Juandan, 2022. "Volatility of clean energy and natural gas, uncertainty indices, and global economic conditions," Energy Economics, Elsevier, vol. 108(C).
    48. 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).
    49. Baumeister, Christiane, 2021. "Measuring Market Expectations," CEPR Discussion Papers 16520, C.E.P.R. Discussion Papers.
    50. Karaki, Mohamad B. & Rangaraju, Sandeep Kumar, 2023. "The confidence channel of U.S. financial uncertainty: Evidence from industry-level data," Economic Modelling, Elsevier, vol. 129(C).
    51. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben, 2023. "Investigating the dynamics of crude oil and clean energy markets in times of geopolitical tensions," Energy Economics, Elsevier, vol. 124(C).
    52. Emanuel Kohlscheen, 2022. "Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices," Papers 2208.14254, arXiv.org, revised Oct 2022.
    53. Rausser, Gordon & Stuermer, Martin, 2020. "A Dynamic Analysis of Collusive Action: The Case of the World Copper Market, 1882-2016," MPRA Paper 104708, University Library of Munich, Germany.
    54. Rangan Gupta & Christian Pierdzioch, 2023. "Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    55. Janus, Jakub, 2022. "Cross-border flights to safe assets in bond markets: evidence from emerging market economies," MPRA Paper 113875, University Library of Munich, Germany.
    56. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," The World Economy, Wiley Blackwell, vol. 45(10), pages 3169-3191, October.
    57. Kassouri, Yacouba & Altıntaş, Halil, 2022. "The quantile dependence of the stock returns of “clean” and “dirty” firms on oil demand and supply shocks," Journal of Commodity Markets, Elsevier, vol. 28(C).
    58. Nida Çakır Melek & Charles W. Calomiris & Harry Mamaysky, 2020. "Mining for Oil Forecasts," Research Working Paper RWP 20-20, Federal Reserve Bank of Kansas City.
    59. Boeckx, Jef & Iania, Leonardo & Wauters, Joris, 2023. "Macroeconomic drivers of Inflation Expectations and Inflation Risk Premia," LIDAM Discussion Papers LFIN 2023003, Université catholique de Louvain, Louvain Finance (LFIN).
    60. Cross, Jamie L. & Hou, Chenghan & Nguyen, Bao H., 2021. "On the China factor in the world oil market: A regime switching approach11We thank Hilde Bjørnland, Tatsuyoshi Okimoto, Ippei Fujiwara, Knut Aastveit, Leif Anders Thorsrud, Francesco Ravazzolo, Renee ," Energy Economics, Elsevier, vol. 95(C).
    61. Jason Brown & Nida Çakır Melek & Johannes Matschke & Sai Sattiraju, 2023. "The Missing Tail Risk in Option Prices," Research Working Paper RWP 23-02, Federal Reserve Bank of Kansas City.
    62. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    63. Zhang, Zhikai & Wang, Yudong & Xiao, Jihong & Zhang, Yaojie, 2023. "Not all geopolitical shocks are alike: Identifying price dynamics in the crude oil market under tensions," Resources Policy, Elsevier, vol. 80(C).
    64. Dimitris Malliaropulos & Petros Migiakis, 2022. "A global monetary policy factor in sovereign bond yields," Working Papers 301, Bank of Greece.
    65. Nguyen, BH & Zhang, Bo, 2022. "Forecasting oil Prices: can large BVARs help?," Working Papers 2022-04, University of Tasmania, Tasmanian School of Business and Economics.
    66. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    67. 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).
    68. Stolbov, Mikhail & Shchepeleva, Maria, 2022. "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    69. Annamaria de Crescenzio & Etienne Lepers, 2021. "Extreme capital flow episodes from the Global Financial Crisis to COVID-19: An exploration with monthly data," OECD Working Papers on International Investment 2021/05, OECD Publishing.
    70. Tom Dudda & Tony Klein & Duc Khuong Nguyen & Thomas Walther, 2022. "Common Drivers of Commodity Futures?," Working Papers 2207, Utrecht School of Economics.
    71. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," Post-Print hal-04027843, HAL.
    72. Chen, Juan & Xiao, Zuoping & Bai, Jiancheng & Guo, Hongling, 2023. "Predicting volatility in natural gas under a cloud of uncertainties," Resources Policy, Elsevier, vol. 82(C).
    73. Sascha A. Keweloh, 2023. "Uncertain Short-Run Restrictions and Statistically Identified Structural Vector Autoregressions," Papers 2303.13281, arXiv.org, revised Apr 2024.
    74. Mikhail I. Stolbov & Maria A. Shchepeleva & Alexander M. Karminsky, 2021. "A global perspective on macroprudential policy interaction with systemic risk, real economic activity, and monetary intervention," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-25, December.
    75. Nonejad, Nima, 2021. "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, vol. 104(C).
    76. Chaturvedi, Priya & Kumar, Kuldeep, 2022. "Econometric modelling of exchange rate volatility using mixed-frequency data," MPRA Paper 115222, University Library of Munich, Germany.
    77. Zhang, Wen, 2022. "China’s government spending and global inflation dynamics: The role of the oil price channel," Energy Economics, Elsevier, vol. 110(C).
    78. Paolo Gelain & Marco Lorusso, 2022. "The US Banks’ Balance Sheet Transmission Channel of Oil Price Shocks," Working Papers 22-33, Federal Reserve Bank of Cleveland.
    79. Li, Zepei & Huang, Haizhen, 2023. "Challenges for volatility forecasts of US fossil energy spot markets during the COVID-19 crisis," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 31-45.
    80. Germán Arana-Landín & Naiara Uriarte-Gallastegi & Beñat Landeta-Manzano & Iker Laskurain-Iturbe, 2023. "The Contribution of Lean Management—Industry 4.0 Technologies to Improving Energy Efficiency," Energies, MDPI, vol. 16(5), pages 1-19, February.
    81. Amor Aniss Benmoussa & Reinhard Ellwanger & Stephen Snudden, 2020. "The New Benchmark for Forecasts of the Real Price of Crude Oil," Staff Working Papers 20-39, Bank of Canada.
    82. Hu, Xiaolu & Yu, Jing & Zhong, Angel, 2023. "The asymmetric effects of oil price shocks on green innovation," Energy Economics, Elsevier, vol. 125(C).
    83. Rangan Gupta & Xin Sheng & Christian Pierdzioch & Qiang Ji, 2021. "Disaggregated Oil Shocks and Stock-Market Tail Risks: Evidence from a Panel of 48 Countries," Working Papers 202106, University of Pretoria, Department of Economics.
    84. Camacho, Maximo & Caro, Angela & Peña, Daniel, 2023. "What drives industrial energy prices?," Economic Modelling, Elsevier, vol. 120(C).

  8. Dimitris Korobilis, 2020. "Sign restrictions in high-dimensional vector autoregressions," Working Papers 2020_21, Business School - Economics, University of Glasgow.

    Cited by:

    1. Martínez-Hernández, Catalina, 2020. "Disentangling the effects of multidimensional monetary policy on inflation and inflation expectations in the euro area," Discussion Papers 2020/18, Free University Berlin, School of Business & Economics.
    2. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    3. Raffaella Giacomini & Toru Kitagawa & Alessio Volpicella, 2017. "Uncertain identification," CeMMAP working papers CWP18/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  9. Gary Koop & Dimitris Korobilis, 2018. "Bayesian dynamic variable selection in high dimensions," Papers 1809.03031, arXiv.org, revised May 2020.

    Cited by:

    1. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2020. "Point and Density Forecasting of Macroeconomic and Financial Uncertainties of the United States," Working Papers 202058, University of Pretoria, Department of Economics.
    2. Mauro Bernardi & Daniele Bianchi & Nicolas Bianco, 2022. "Smoothing volatility targeting," Papers 2212.07288, arXiv.org.
    3. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    4. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    5. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    6. Salisu, Afees A. & Tchankam, Jean Paul, 2022. "US Stock return predictability with high dimensional models," Finance Research Letters, Elsevier, vol. 45(C).
    7. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    8. Zhao, Jing, 2023. "Time-varying impact of geopolitical risk on natural resources prices: Evidence from the hybrid TVP-VAR model with large system," Resources Policy, Elsevier, vol. 82(C).
    9. Luo, Jiawen & Klein, Tony & Walther, Thomas & Ji, Qiang, 2021. "Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning," QBS Working Paper Series 2021/04, Queen's University Belfast, Queen's Business School.
    10. 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.
    11. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    12. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2021. "Point and density forecasting of macroeconomic and financial uncertainties of the USA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 700-707, July.

  10. Korobilis, D & Yilmaz, K, 2018. "Measuring Dynamic Connectedness with Large Bayesian VAR Models," Essex Finance Centre Working Papers 20937, University of Essex, Essex Business School.

    Cited by:

    1. Eddie Gerba & Danilo Leiva-Leon, 2020. "Macro-financial interactions in a changing world," Working Papers 2018, Banco de España.
    2. Ioannis Chatziantoniou & David Gabauer, 2019. "EMU-Risk Synchronisation and Financial Fragility Through the Prism of Dynamic Connectedness," Working Papers in Economics & Finance 2019-07, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    3. Jiasha Fu & Hui Qiao, 2022. "The Time-Varying Connectedness Between China’s Crude Oil Futures and International Oil Markets: A Return and Volatility Spillover Analysis," Letters in Spatial and Resource Sciences, Springer, vol. 15(3), pages 341-376, December.
    4. M. Raddant & T. Di Matteo, 2023. "A Look at Financial Dependencies by Means of Econophysics and Financial Economics," Papers 2302.08208, arXiv.org.
    5. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan, 2019. "International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression," International Review of Financial Analysis, Elsevier, vol. 65(C).
    6. Feng, Huiqun & Zhang, Jun & Guo, Na, 2023. "Time-varying linkages between energy and stock markets: Dynamic spillovers and driving factors," International Review of Financial Analysis, Elsevier, vol. 89(C).
    7. Elsayed, Ahmed H. & Gozgor, Giray & Yarovaya, Larisa, 2022. "Volatility and return connectedness of cryptocurrency, gold, and uncertainty: Evidence from the cryptocurrency uncertainty indices," Finance Research Letters, Elsevier, vol. 47(PB).
    8. Wang, Peiwan & Zong, Lu, 2020. "Contagion effects and risk transmission channels in the housing, stock, interest rate and currency markets: An Empirical Study in China and the U.S," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    9. Ali, Shoaib & Ijaz, Muhammad Shahzad & Yousaf, Imran, 2023. "Dynamic spillovers and portfolio risk management between defi and metals: Empirical evidence from the Covid-19," Resources Policy, Elsevier, vol. 83(C).
    10. Song, Lu & Tian, Gengyu & Jiang, Yonghong, 2022. "Connectedness of commodity, exchange rate and categorical economic policy uncertainties — Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    11. Assaf, Ata & Charif, Husni & Mokni, Khaled, 2021. "Dynamic connectedness between uncertainty and energy markets: Do investor sentiments matter?," Resources Policy, Elsevier, vol. 72(C).
    12. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2023. "Does economic policy uncertainty drive the dynamic spillover among traditional currencies and cryptocurrencies? The role of the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 64(C).
    13. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2021. "A regional decomposition of US housing prices and volume: market dynamics and Portfolio diversification," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(2), pages 279-307, April.
    14. Yakup Arı, 2022. "TVP-VAR Based CARR-Volatility Connectedness: Evidence from The Russian-Ukraine Conflict," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 7(3), pages 590-607.
    15. Hedi Ben Haddad & Imed Mezghani & Abdessalem Gouider, 2021. "The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties," Economies, MDPI, vol. 9(2), pages 1-22, June.
    16. Manel Youssef & Khaled Mokni & Ahdi Noomen Ajmi, 2021. "Dynamic connectedness between stock markets in the presence of the COVID-19 pandemic: does economic policy uncertainty matter?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    17. Jose Arreola Hernandez & Sang Hoon Kang & Seong-Min Yoon, 2022. "Spillovers and portfolio optimization of precious metals and global/regional equity markets," Applied Economics, Taylor & Francis Journals, vol. 54(20), pages 2320-2342, April.
    18. Elsayed, Ahmed H. & Gozgor, Giray & Lau, Chi Keung Marco, 2022. "Risk transmissions between bitcoin and traditional financial assets during the COVID-19 era: The role of global uncertainties," International Review of Financial Analysis, Elsevier, vol. 81(C).
    19. Joshua C.C. Chan & Xuewen Yu, 2020. "Fast and accurate variational inference for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2020-108, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    20. Elsayed, Ahmed H. & Sousa, Ricardo M., 2022. "International monetary policy and cryptocurrency markets: dynamic and spillover effects," LSE Research Online Documents on Economics 115305, London School of Economics and Political Science, LSE Library.
    21. Gabauer, David, 2021. "Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system," Journal of Multinational Financial Management, Elsevier, vol. 60(C).
    22. Liu, Tangyong & Gong, Xu, 2020. "Analyzing time-varying volatility spillovers between the crude oil markets using a new method," Energy Economics, Elsevier, vol. 87(C).
    23. Sophie Béreau & Nicolas Debarsy & Cyrille Dossougoin & Jean-Yves Gnabo, 2022. "Contagion in the Banking Industry: a Robust-to-Endogeneity Analysis," Working Papers halshs-03513049, HAL.
    24. Umar, Zaghum & Manel, Youssef & Riaz, Yasir & Gubareva, Mariya, 2021. "Return and volatility transmission between emerging markets and US debt throughout the pandemic crisis," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    25. Arı, Yakup, 2022. "USD/TRY and foreign banks in Turkey: Evidence by TVP-VAR," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 5-26.
    26. Lukas Boeckelmann & Arthur Stalla-Bourdillon, 2021. "Structural Estimation of Time-Varying Spillovers:an Application to International Credit Risk Transmission," Working Papers hal-03338209, HAL.
    27. Mahdi Ghaemi Asl & Oluwasegun B. Adekoya & Muhammad Mahdi Rashidi, 2023. "Quantiles dependence and dynamic connectedness between distributed ledger technology and sectoral stocks: enhancing the supply chain and investment decisions with digital platforms," Annals of Operations Research, Springer, vol. 327(1), pages 435-464, August.
    28. Tuncer Murathan & Akbulut Nesrin & Turhan Miraç Savaş & Ari Yakup, 2022. "Time-Varying Network Connectedness Between the Organizational Ecology of Transportation and Storage Firms and Macroeconomic Variables," Folia Oeconomica Stetinensia, Sciendo, vol. 22(2), pages 209-223, December.
    29. Huynh, Toan Luu Duc & Foglia, Matteo & Nasir, Muhammad Ali & Angelini, Eliana, 2021. "Feverish sentiment and global equity markets during the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 1088-1108.
    30. Gabauer, David & Gupta, Rangan, 2020. "Spillovers across macroeconomic, financial and real estate uncertainties: A time-varying approach," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 167-173.
    31. Zhang, Yulian & Hamori, Shigeyuki, 2021. "Do news sentiment and the economic uncertainty caused by public health events impact macroeconomic indicators? Evidence from a TVP-VAR decomposition approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 145-162.
    32. Yousaf, Imran & Jareño, Francisco & Tolentino, Marta, 2023. "Connectedness between Defi assets and equity markets during COVID-19: A sector analysis," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    33. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2019. "Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness," Working Papers ECARES 2019-09, ULB -- Universite Libre de Bruxelles.
    34. Yousaf, Imran & Yarovaya, Larisa, 2022. "Static and dynamic connectedness between NFTs, Defi and other assets: Portfolio implication," Global Finance Journal, Elsevier, vol. 53(C).
    35. Zhou, Dong-hai & Liu, Xiao-xing & Tang, Chun & Yang, Guang-yi, 2023. "Time-varying risk spillovers in Chinese stock market – New evidence from high-frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    36. Marta Gómez-Puig & Mary Pieterse-Bloem & Simón Sosvilla-Rivero, 2022. ""Dynamic connectedness between credit and liquidity risks in EMU sovereign debt markets"," IREA Working Papers 202217, University of Barcelona, Research Institute of Applied Economics, revised Oct 2022.
    37. Sowmya Subramaniam & David Gabauer & Rangan Gupta, 2018. "On the Transmission Mechanism of Asia-Pacific Yield Curve Characteristics," Working Papers 201864, University of Pretoria, Department of Economics.
    38. Chan, Ying Tung & Qiao, Hui, 2023. "Volatility spillover between oil and stock prices: Structural connectedness based on a multi-sector DSGE model approach with Bayesian estimation," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 265-286.
    39. Ioannis Chatziantoniou & David Gabauer & Alexis Stenfors, 2019. "From CIP-Deviations to a Market for Risk Premia: A Dynamic Investigation of Cross-Currency Basis Swaps," Working Papers in Economics & Finance 2019-05, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    40. Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2019. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," EMF Research Papers 20, Economic Modelling and Forecasting Group.
    41. So Jung Hwang & Hyunduk Suh, 2021. "Analyzing Dynamic Connectedness in Korean Housing Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(2), pages 591-609, January.
    42. Papież, Monika & Rubaszek, Michał & Szafranek, Karol & Śmiech, Sławomir, 2022. "Are European natural gas markets connected? A time-varying spillovers analysis," Resources Policy, Elsevier, vol. 79(C).
    43. Gómez-Puig, Marta & Pieterse-Bloem, Mary & Sosvilla-Rivero, Simón, 2023. "Dynamic connectedness between credit and liquidity risks in euro area sovereign debt markets," Journal of Multinational Financial Management, Elsevier, vol. 68(C).
    44. Chunyi Lu & Zhuoqi Teng & Yu Gao & Renhong Wu & Md. Alamgir Hossain & Yuantao Fang, 2022. "Analysis of Early Warning of RMB Exchange Rate Fluctuation and Value at Risk Measurement Based on Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1501-1524, April.
    45. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    46. Marius Cristian Acatrinei, 2020. "Spillover index for European business cycle," Journal of Financial Studies, Institute of Financial Studies, vol. 9(5), pages 49-57, November.
    47. Zhang, Wenting & He, Xie & Hamori, Shigeyuki, 2022. "Volatility spillover and investment strategies among sustainability-related financial indexes: Evidence from the DCC-GARCH-based dynamic connectedness and DCC-GARCH t-copula approach," International Review of Financial Analysis, Elsevier, vol. 83(C).
    48. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
    49. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    50. Yao Xiao & Zibing Dong & Shihua Huang & Yanshuang Li & Jian Wang & Xintian Zhuang & Stefan Cristian Gherghina, 2023. "Time-Frequency Volatility Spillovers among Major International Financial Markets: Perspective from Global Extreme Events," Discrete Dynamics in Nature and Society, Hindawi, vol. 2023, pages 1-20, May.
    51. Y'erali Gandica & Sophie B'ereau & Jean-Yves Gnabo, 2019. "A multilevel analysis to systemic exposure: insights from local and system-wide information," Papers 1910.08611, arXiv.org.
    52. Dong, Zibing & Li, Yanshuang & Zhuang, Xintian & Wang, Jian, 2022. "Impacts of COVID-19 on global stock sectors: Evidence from time-varying connectedness and asymmetric nexus analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    53. Patel, Ritesh & Kumar, Sanjeev & Bouri, Elie & Iqbal, Najaf, 2023. "Spillovers between green and dirty cryptocurrencies and socially responsible investments around the war in Ukraine," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 143-162.

  11. Korobilis, Dimitris & Koop, Gary, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," Essex Finance Centre Working Papers 22665, University of Essex, Essex Business School.

    Cited by:

    1. 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.
    2. David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin & Bonsoo Koo, 2021. "Loss-Based Variational Bayes Prediction," Monash Econometrics and Business Statistics Working Papers 8/21, Monash University, Department of Econometrics and Business Statistics.
    3. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    4. Deborah Gefang & Gary Koop & Aubrey Poon, "undated". "Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs," Discussion Papers in Economics 20/02, Division of Economics, School of Business, University of Leicester.
    5. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Joshua C.C. Chan & Xuewen Yu, 2020. "Fast and accurate variational inference for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2020-108, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Chaya Weerasinghe & Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier, 2023. "ABC-based Forecasting in State Space Models," Monash Econometrics and Business Statistics Working Papers 12/23, Monash University, Department of Econometrics and Business Statistics.
    8. Reza Hajargasht, 2019. "Approximation Properties of Variational Bayes for Vector Autoregressions," Papers 1903.00617, arXiv.org.
    9. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-07, Economic Statistics Centre of Excellence (ESCoE).
    10. Badi H. Baltagi & Georges Bresson & Jean-Michel Etienne, 2020. "Growth Empirics: a Bayesian Semiparametric Model With Random Coefficients for a Panel of OECD Countries," Advances in Econometrics, in: Essays in Honor of Cheng Hsiao, volume 41, pages 217-253, Emerald Group Publishing Limited.
    11. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    12. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    13. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    14. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    15. Gael M. Martin & David T. Frazier & Christian P. Robert, 2021. "Approximating Bayes in the 21st Century," Monash Econometrics and Business Statistics Working Papers 24/21, Monash University, Department of Econometrics and Business Statistics.
    16. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Efficient variational approximations for state space models," Papers 2210.11010, arXiv.org, revised Jun 2023.
    17. 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.

  12. Koop, G & Korobilis, D, 2018. "Forecasting with High-Dimensional Panel VARs," Essex Finance Centre Working Papers 21329, University of Essex, Essex Business School.

    Cited by:

    1. Laura Liu & Christian Matthes & Katerina Petrova, 2018. "Monetary Policy across Space and Time," Working Paper 18-14, Federal Reserve Bank of Richmond.
    2. Francesco Simone Lucidi, 2023. "The misalignment of fiscal multipliers in Italian regions," Regional Studies, Taylor & Francis Journals, vol. 57(10), pages 2073-2086, October.
    3. Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Macroeconomic forecasting in a multi‐country context," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1230-1255, September.
    4. Christou, Christina & Cunado, Juncal & Gupta, Rangan & Hassapis, Christis, 2017. "Economic policy uncertainty and stock market returns in PacificRim countries: Evidence based on a Bayesian panel VAR model," Journal of Multinational Financial Management, Elsevier, vol. 40(C), pages 92-102.
    5. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2019. "The impact of economic policy uncertainty and commodity prices on CARB country stock market volatility," MPRA Paper 96577, University Library of Munich, Germany.
    6. Costa-Font, Joan & Vilaplana-Prieto, Cristina, 2023. "Investing in care for old age? An examination of long-term care expenditure dynamics and its spillovers," LSE Research Online Documents on Economics 113582, London School of Economics and Political Science, LSE Library.
    7. Florian Huber & Gary Koop, 2021. "Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions," Papers 2107.07804, arXiv.org.
    8. Christina Christou & Rangan Gupta & Christis Hassapis, 2016. "Does Economic Policy Uncertainty Forecast Real Housing Returns in a Panel of OECD Countries? A Bayesian Approach," Working Papers 201637, University of Pretoria, Department of Economics.
    9. Camehl, Annika & von Schweinitz, Gregor, 2023. "What explains international interest rate co-movement?," IWH Discussion Papers 3/2023, Halle Institute for Economic Research (IWH), revised 2023.
    10. Bebonchu Atems, 2020. "Identifying the Dynamic Effects of Income Inequality on Crime," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 751-782, August.
    11. Martinez-Miera, David & Repullo, Rafael, 2019. "Monetary policy, macroprudential policy, and financial stability," Working Paper Series 2297, European Central Bank.
    12. 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.
    13. Simona Malovana & Jan Frait, 2016. "Monetary Policy and Macroprudential Policy: Rivals or Teammates?," Working Papers IES 2016/19, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2016.
    14. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-07, Economic Statistics Centre of Excellence (ESCoE).
    15. Davidson, Sharada Nia, 2020. "Interdependence or contagion: A model switching approach with a focus on Latin America," Economic Modelling, Elsevier, vol. 85(C), pages 166-197.
    16. Florian Huber & Michael Pfarrhofer & Philipp Piribauer, 2020. "A multi‐country dynamic factor model with stochastic volatility for euro area business cycle analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 911-926, September.
    17. Alexey Ponomarenko & Anna Rozhkova & Sergei Seleznev, 2017. "Macro-financial linkages: the role of liquidity dependence," Bank of Russia Working Paper Series wps24, Bank of Russia.
    18. Florian Huber & Tam'as Krisztin & Michael Pfarrhofer, 2018. "A Bayesian panel VAR model to analyze the impact of climate change on high-income economies," Papers 1804.01554, arXiv.org, revised Feb 2021.
    19. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
    20. Joscha Beckmann & Robert Czudaj, 2017. "Capital Flows and GDP in Emerging Economies and the Role of Global Spillovers," Chemnitz Economic Papers 009, Department of Economics, Chemnitz University of Technology, revised Jun 2017.
    21. Sheereen Fauzel* & Boopen Seetanah & RV Sannassee, 2015. "Foreign direct investment and welfare nexus in sub Saharan Africa," Journal of Developing Areas, Tennessee State University, College of Business, vol. 49(4), pages 271-283, October-D.
    22. Schnücker, A.M., 2019. "Penalized Estimation of Panel Vector Autoregressive Models," Econometric Institute Research Papers EI-2019-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    23. Marcellino, Massimiliano & Foroni, Claudia & Casarin, Roberto & Ravazzolo, Francesco, 2017. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," CEPR Discussion Papers 12339, C.E.P.R. Discussion Papers.
    24. Beechey, Meredith & Österholm, Pär & Poon, Aubrey, 2023. "Estimating the US trend short-term interest rate," Finance Research Letters, Elsevier, vol. 55(PA).

  13. Korobilis, Dimitris, 2018. "Machine Learning Macroeconometrics A Primer," Essex Finance Centre Working Papers 22666, University of Essex, Essex Business School.

    Cited by:

    1. Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 81568, University Library of Munich, Germany.

  14. Dimitris Korobilis & Davide Pettenuzzo, 2017. "Adaptive Hierarchical Priors for High-Dimensional Vector Autoregessions," Working Papers 115, Brandeis University, Department of Economics and International Business School.

    Cited by:

    1. Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Macroeconomic forecasting in a multi‐country context," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1230-1255, September.
    2. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    3. 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.
    4. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    5. Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Working Papers 130, Brandeis University, Department of Economics and International Business School.
    6. Florian Huber & Gary Koop, 2021. "Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions," Papers 2107.07804, arXiv.org.
    7. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
    8. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.
    9. Joshua C. C. Chan, 2019. "Asymmetric conjugate priors for large Bayesian VARs," CAMA Working Papers 2019-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Yuan Yan & Hsin-Cheng Huang & Marc G. Genton, 2021. "Vector Autoregressive Models with Spatially Structured Coefficients for Time Series on a Spatial Grid," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 387-408, September.
    11. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    12. Joshua C. C. Chan, 2019. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," CAMA Working Papers 2019-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. Follett, Lendie & Yu, Cindy, 2019. "Achieving parsimony in Bayesian vector autoregressions with the horseshoe prior," Econometrics and Statistics, Elsevier, vol. 11(C), pages 130-144.
    14. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
    15. Gupta, Rangan & Sun, Xiaojin, 2020. "Forecasting economic policy uncertainty of BRIC countries using Bayesian VARs," Economics Letters, Elsevier, vol. 186(C).
    16. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    17. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    18. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    19. Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021. "A mixed frequency BVAR for the euro area labour market," Working Paper Series 2601, European Central Bank.
    20. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    21. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    22. Prüser, Jan, 2023. "Data-based priors for vector error correction models," International Journal of Forecasting, Elsevier, vol. 39(1), pages 209-227.
    23. Prüser, Jan & Blagov, Boris, 2022. "Improving inference and forecasting in VAR models using cross-sectional information," Ruhr Economic Papers 960, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

  15. Beckmann, J & Koop, G & Korobilis, D & Schüssler, R, 2017. "Exchange rate predictability and dynamic Bayesian learning," Essex Finance Centre Working Papers 20781, University of Essex, Essex Business School.

    Cited by:

    1. 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.
    2. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    3. 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.
    4. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    5. Niklas Valentin Lehmann, 2023. "Forecasting skill of a crowd-prediction platform: A comparison of exchange rate forecasts," Papers 2312.09081, arXiv.org.
    6. Hauzenberger, Niko & Huber, Florian, 2018. "Model instability in predictive exchange rate regressions," Working Papers in Economics 2018-8, University of Salzburg.
    7. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
    8. Svatopluk Kapounek & Zuzana Kučerová & Evžen Kočenda, 2022. "Selective Attention in Exchange Rate Forecasting," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(2), pages 210-229, May.
    9. Yuhyeon Bak & Cheolbeom Park, 2020. "Exchange Rate Predictability, Risk Premiums, and Predictive System," Discussion Paper Series 2006, Institute of Economic Research, Korea University.
    10. Sheng, Xin & Gupta, Rangan & Salisu, Afees A. & Bouri, Elie, 2022. "OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning," Finance Research Letters, Elsevier, vol. 45(C).
    11. Davidson, Sharada Nia, 2020. "Interdependence or contagion: A model switching approach with a focus on Latin America," Economic Modelling, Elsevier, vol. 85(C), pages 166-197.
    12. Bruno P. C. Levy & Hedibert F. Lopes, 2021. "Dynamic Ordering Learning in Multivariate Forecasting," Papers 2101.04164, arXiv.org, revised Nov 2021.
    13. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
    14. Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2022. "Bayesian Neural Networks for Macroeconomic Analysis," Papers 2211.04752, arXiv.org, revised Apr 2024.
    15. Nonejad, Nima, 2020. "Crude oil price volatility and equity return predictability: A comparative out-of-sample study," International Review of Financial Analysis, Elsevier, vol. 71(C).

  16. Luca Gambetti & Dimitris Korobilis & John D. Tsoukalas & Francesco Zanetti, 2017. "The Effect of News Shocks and Monetary Policy," BCAM Working Papers 1705, Birkbeck Centre for Applied Macroeconomics.

    Cited by:

    1. Di Casola, Paola & Sichlimiris, Spyridon, 2018. "Towards Technology-News-Driven Business Cycles," Working Paper Series 360, Sveriges Riksbank (Central Bank of Sweden).
    2. Rick Van der Ploeg & Fidel Perez-Sebastian & Ohad Raveh, 2019. "Oil Discoveries and Protectionism," Economics Series Working Papers 895, University of Oxford, Department of Economics.
    3. Laura E. Jackson & Michael T. Owyang & Daniel Soques, 2016. "Nonlinearities, Smoothing and Countercyclical Monetary Policy," Working Papers 2016-8, Federal Reserve Bank of St. Louis.
    4. Bretscher, Lorenzo & Malkhozov, Aytek & Tamoni, Andrea, 2021. "Expectations and aggregate risk," Journal of Monetary Economics, Elsevier, vol. 123(C), pages 91-108.
    5. Lorenzo Bretscher & Andrea Tamoni & Aytek Malkhozov, 2019. "News Shocks and Asset Prices," 2019 Meeting Papers 100, Society for Economic Dynamics.
    6. Fidel Sebastian-Perez & Ohad Raveh & Rick van der Ploeg, 2021. "Oil discoveries and protectionism: role of news effects," Tinbergen Institute Discussion Papers 21-047/VIII, Tinbergen Institute.
    7. Silvia Miranda-Agrippino & Sinem Hacioglu Hoke & Kristina Bluwstein, 2018. "When Creativity Strikes: News Shocks and Business Cycle Fluctuations," Discussion Papers 1823, Centre for Macroeconomics (CFM).
    8. Letendre, Marc-André & Obaid, Sabreena, 2020. "Emerging economy business cycles: Interest rate shocks vs trend shocks," Economic Modelling, Elsevier, vol. 93(C), pages 526-545.
    9. Beqiraj, Elton & Di Bartolomeo, Giovanni & Di Pietro, Marco & Serpieri, Carolina, 2018. "Comparing Central Europe and the Baltic macro-economies: A Bayesian approach," EconStor Preprints 175242, ZBW - Leibniz Information Centre for Economics.
    10. Harrison, Richard & Waldron, Matt, 2021. "Optimal policy with occasionally binding constraints: piecewise linear solution methods," Bank of England working papers 911, Bank of England.

  17. Korobilis, D, 2017. "Forecasting with many predictors using message passing algorithms," Essex Finance Centre Working Papers 19565, University of Essex, Essex Business School.

    Cited by:

    1. 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.
    2. 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.
    3. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    4. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    5. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    6. 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).
    7. 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.
    8. Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Working Papers 130, Brandeis University, Department of Economics and International Business School.
    9. 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.
    10. Boriss Siliverstovs & Daniel Wochner, 2019. "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," KOF Working papers 19-463, KOF Swiss Economic Institute, ETH Zurich.
    11. 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.
    12. 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.
    13. 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.
    14. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    15. 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.
    16. 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.
    17. 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.

  18. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Business School.

    Cited by:

    1. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    2. Mike G. Tsionas, 2016. "Alternatives to large VAR, VARMA and multivariate stochastic volatility models," Working Papers 217, Bank of Greece.
    3. 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.
    4. Silvia Miranda Agrippino & Giovanni Ricco, 2018. "Bayesian vector autoregressions," SciencePo Working papers Main hal-03458277, HAL.
    5. Rangan Gupta & Chi Keung Marco Lau & Vasilios Plakandaras & Wing-Keung Wong, 2019. "The role of housing sentiment in forecasting U.S. home sales growth: evidence from a Bayesian compressed vector autoregressive model," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pages 2554-2567, January.
    6. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    7. Dimitris Korobilis & Davide Pettenuzzo, 2018. "Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions," Working Paper series 18-21, Rimini Centre for Economic Analysis.
    8. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    9. Joshua C.C. Chan & Rodney W. Strachan, 2020. "Bayesian state space models in macroeconometrics," CAMA Working Papers 2020-90, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Emmanuel C. Mamatzakis & Steven Ongena & Mike G. Tsionas, 2023. "The response of household debt to COVID-19 using a neural networks VAR in OECD," Empirical Economics, Springer, vol. 65(1), pages 65-91, July.
    11. Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Working Papers 130, Brandeis University, Department of Economics and International Business School.
    12. Cross, Jamie, 2019. "On the reduced macroeconomic volatility of the Australian economy: Good policy or good luck?," Economic Modelling, Elsevier, vol. 77(C), pages 174-186.
    13. Carlos Carvalho & Jared D. Fisher & Davide Pettenuzzo, 2018. "Optimal Asset Allocation with Multivariate Bayesian Dynamic Linear Models," Working Papers 123, Brandeis University, Department of Economics and International Business School.
    14. Koop, G & Korobilis, D, 2018. "Forecasting with High-Dimensional Panel VARs," Essex Finance Centre Working Papers 21329, University of Essex, Essex Business School.
    15. Mike G. Tsionas, 2016. "Alternative Bayesian compression in Vector Autoregressions and related models," Working Papers 216, Bank of Greece.
    16. Tom Boot & Didier Nibbering, 2016. "Forecasting Using Random Subspace Methods," Tinbergen Institute Discussion Papers 16-073/III, Tinbergen Institute, revised 11 Aug 2017.
    17. Assaf, A. George & Tsionas, Mike G., 2019. "Forecasting occupancy rate with Bayesian compression methods," Annals of Tourism Research, Elsevier, vol. 75(C), pages 439-449.
    18. Maximilian Böck & Martin Feldkircher & Pierre L. Siklos, 2021. "International Effects of Euro Area Forward Guidance," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(5), pages 1066-1110, October.
    19. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    20. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    21. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
    22. 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.
    23. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
    24. Minerva Mukhopadhyay & David B. Dunson, 2020. "Targeted Random Projection for Prediction From High-Dimensional Features," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1998-2010, December.
    25. Crespo Cuaresma, Jesus & Doppelhofer, Gernot & Feldkircher, Martin & Huber, Florian, 2018. "Spillovers from US monetary policy: Evidence from a time-varying parameter GVAR model," Working Papers in Economics 2018-6, University of Salzburg.
    26. Gupta, Rangan & Sun, Xiaojin, 2020. "Forecasting economic policy uncertainty of BRIC countries using Bayesian VARs," Economics Letters, Elsevier, vol. 186(C).
    27. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    28. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2021. "Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs," Papers 2103.04944, arXiv.org, revised Feb 2022.
    29. Daniel Felix Ahelegbey & Luis Carvalho & Eric D. Kolaczyk, 2020. "A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series," DEM Working Papers Series 181, University of Pavia, Department of Economics and Management.
    30. Lusompa, Amaze, 2019. "Local Projections, Autocorrelation, and Efficiency," MPRA Paper 99856, University Library of Munich, Germany, revised 11 Apr 2020.
    31. Shabeer Khan & Mirzat Ullah & Mohammad Rahim Shahzad & Uzair Abdullah Khan & Umair Khan & Sayed M. Eldin & Abeer M. Alotaibi, 2022. "Spillover Connectedness among Global Uncertainties and Sectorial Indices of Pakistan: Evidence from Quantile Connectedness Approach," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    32. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    33. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    34. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    35. 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.
    36. Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.
    37. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    38. Jesús Crespo Cuaresma & Gernot Doppelhofer & Martin Feldkircher & Florian Huber, 2019. "Spillovers from US monetary policy: evidence from a time varying parameter global vector auto‐regressive model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 831-861, June.
    39. Joshua C. C. Chan, 2019. "Large Bayesian vector autoregressions," CAMA Working Papers 2019-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    40. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2020. "Modeling Turning Points In Global Equity Market," DEM Working Papers Series 195, University of Pavia, Department of Economics and Management.
    41. Spyros Makridakis & Andreas Merikas & Anna Merika & Mike G. Tsionas & Marwan Izzeldin, 2020. "A novel forecasting model for the Baltic dry index utilizing optimal squeezing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 56-68, January.

  19. Korobilis, D & Pettenuzzo, D, 2016. "Adaptive Minnesota Prior for High-Dimensional Vector Autoregressions," Essex Finance Centre Working Papers 18626, University of Essex, Essex Business School.

    Cited by:

    1. Feldkircher, Martin & Kastner, Gregor & Huber, Florian, 2018. "Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?," Department of Economics Working Paper Series 260, WU Vienna University of Economics and Business.
    2. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
    3. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: a multi-country BVAR benchmark for the Eurosystem projections," Working Paper Series 2227, European Central Bank.

  20. Byrne, JP & Cao, S & Korobilis, D, 2016. "Decomposing Global Yield Curve Co-Movement," Essex Finance Centre Working Papers 18194, University of Essex, Essex Business School.

    Cited by:

    1. Umar, Zaghum & Riaz, Yasir & Aharon, David Y., 2022. "Network connectedness dynamics of the yield curve of G7 countries," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 275-288.
    2. María Nieves López-García & Miguel Angel Sánchez-Granero & Juan Evangelista Trinidad-Segovia & Antonio Manuel Puertas & Francisco Javier De las Nieves, 2021. "Volatility Co-Movement in Stock Markets," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    3. Petter Eilif de Lange & Morten Risstad & Kristian Semmen & Sjur Westgaard, 2023. "Term Premia in Norwegian Interest Rate Swaps," JRFM, MDPI, vol. 16(3), pages 1-19, March.
    4. Berndt Jesenko & Christian Schlögl, 2021. "The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6785-6801, August.
    5. Demetri Tsanacas, 2022. "Valuation Challenges in High Tech Platform Based Corporations," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 89-100.
    6. Inaba, Kei-Ichiro, 2021. "An empirical illustration of the integration of sovereign bond markets," Journal of Multinational Financial Management, Elsevier, vol. 61(C).
    7. Venetis, Ioannis & Ladas, Avgoustinos, 2022. "Co-movement and global factors in sovereign bond yields," MPRA Paper 115801, University Library of Munich, Germany.
    8. Kei-Ichiro Inaba, 2020. "The Integration of Countries' Sovereign Bond Markets: An Empirical Illustration of a Global Financial Cycle," IMES Discussion Paper Series 20-E-01, Institute for Monetary and Economic Studies, Bank of Japan.
    9. Jamie L. Cross & Aubrey Poon & Dan Zhu, 2023. "Uncertainty and the Term Structure of Interest Rates," Working Papers No 12/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

  21. P. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," SIRE Discussion Papers 2015-71, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.

  22. Korobilis, Dimitris, 2015. "Quantile forecasts of inflation under model uncertainty," SIRE Discussion Papers 2015-72, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios & Gupta, Rangan, 2019. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," DUTH Research Papers in Economics 3-2016, Democritus University of Thrace, Department of Economics.
    2. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.

  23. BAUWENS, Luc & KOOP, Gary & KOROBILIS, Dimitris & ROMBOUTS, Jeroen, 2015. "The Contribution of Structural Break Models to Forecating Macroeconomic Series," LIDAM Reprints CORE 2651, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Nonejad Nima, 2015. "Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 561-584, December.
    2. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    3. Pierre L. Siklos, 2020. "Looking into the Rear-View Mirror: Lessons from Japan for the Eurozone and the U.S?," IMES Discussion Paper Series 20-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
    4. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2020. "Point and Density Forecasting of Macroeconomic and Financial Uncertainties of the United States," Working Papers 202058, University of Pretoria, Department of Economics.
    5. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
    6. Todd E. Clark & Taeyoung Doh, 2011. "A Bayesian evaluation of alternative models of trend inflation," Research Working Paper RWP 11-16, Federal Reserve Bank of Kansas City.
    7. Wensheng Kang & Jing Wang, 2018. "Oil shocks, policy uncertainty and earnings surprises," Review of Quantitative Finance and Accounting, Springer, vol. 51(2), pages 375-388, August.
    8. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    9. 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.
    10. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    11. Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2015. "Forecasting with VAR Models: Fat Tails and Stochastic Volatility," CReMFi Discussion Papers 2, CReMFi, School of Economics and Finance, QMUL.
    12. Gary Koop & Dimitris Korompilis, 2009. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," Working Papers 0917, University of Strathclyde Business School, Department of Economics.
    13. Hossein Hassani & Zara Ghodsi & Rangan Gupta & Mawuli K. Segnon, 2014. "Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis," Working Papers 201482, University of Pretoria, Department of Economics.
    14. Ardia, David & Dufays, Arnaud & Ordás Criado, Carlos, 2023. "Linking Frequentist and Bayesian Change-Point Methods," MPRA Paper 119486, University Library of Munich, Germany.
    15. Luis Uzeda, 2016. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," ANU Working Papers in Economics and Econometrics 2016-632, Australian National University, College of Business and Economics, School of Economics.
    16. Koop, G & Korobilis, D, 2018. "Forecasting with High-Dimensional Panel VARs," Essex Finance Centre Working Papers 21329, University of Essex, Essex Business School.
    17. McKnight, Stephen & Mihailov, Alexander & Rumler, Fabio, 2020. "Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend," Economic Modelling, Elsevier, vol. 87(C), pages 383-393.
    18. Jiang, Feiyu & Zhao, Zifeng & Shao, Xiaofeng, 2023. "Time series analysis of COVID-19 infection curve: A change-point perspective," Journal of Econometrics, Elsevier, vol. 232(1), pages 1-17.
    19. Arnaud Dufays & Jeroen V. K. Rombouts, 2019. "Sparse Change-point HAR Models for Realized Variance," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 857-880, September.
    20. Markku Lanne & Jani Luoto, 2017. "A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 969-995, August.
    21. Hou, Chenghan, 2017. "Infinite hidden markov switching VARs with application to macroeconomic forecast," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1025-1043.
    22. Czinkota, Thomas, 2012. "Das Halteproblem bei Strukturbrüchen in Finanzmarktzeitreihen [The Halting Problem applied to Structural Breaks in Financial Time Series]," MPRA Paper 37072, University Library of Munich, Germany.
    23. Wensheng Kang & Ronald A. Ratti & Kyung Hwan Yoon, 2015. "Time-varying effect of oil market shocks on the stock market," CAMA Working Papers 2015-35, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    24. Kirsten Thompson & Renee Van Eyden & Rangan Gupta, 2015. "Identifying an index of financial conditions for South Africa," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(2), pages 256-274, June.
    25. Timmermann, Allan & Pettenuzzo, Davide, 2016. "Forecasting Macroeconomic Variables under Model Instability," CEPR Discussion Papers 11355, C.E.P.R. Discussion Papers.
    26. Kascha, Christian & Trenkler, Carsten, 2015. "Forecasting VARs, model selection, and shrinkage," Working Papers 15-07, University of Mannheim, Department of Economics.
    27. 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.
    28. Mark Fisher & Mark J. Jensen, 2018. "Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors," Working Paper series 18-12, Rimini Centre for Economic Analysis.
    29. Nonejad, Nima, 2020. "Crude oil price changes and the United Kingdom real gross domestic product growth rate: An out-of-sample investigation," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    30. Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2023. "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms," Papers 2303.01887, arXiv.org.
    31. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
    32. Dimitrios P. Louzis, 2016. "Macroeconomic forecasting and structural changes in steady states," Working Papers 204, Bank of Greece.
    33. Bauwens, Luc & De Backer, Bruno & Dufays, Arnaud, 2014. "A Bayesian method of change-point estimation with recurrent regimes: Application to GARCH models," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 207-229.
    34. Korobilis, D, 2017. "Forecasting with many predictors using message passing algorithms," Essex Finance Centre Working Papers 19565, University of Essex, Essex Business School.
    35. C. Y. Tan & Y. B. Koh & K. H. Ng & K. H. Ng, 2019. "Structural Change Analysis of Active Cryptocurrency Market," Papers 1909.10679, arXiv.org.
    36. Gary Koop & Dimitris Korobilis, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," Working Paper series 18-31, Rimini Centre for Economic Analysis.
    37. Markku Lanne & Jani Luoto, 2014. "Noncausal Bayesian Vector Autoregression," CREATES Research Papers 2014-07, Department of Economics and Business Economics, Aarhus University.
    38. Kirsten Thompson & Reneé van Eyden & Rangan Gupta, 2015. "Testing the Out-of-Sample Forecasting Ability of a Financial Conditions Index for South Africa," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(3), pages 486-501, May.
    39. Dimitris Korobilis, 2019. "High-dimensional macroeconomic forecasting using message passing algorithms," Working Papers 2019_07, Business School - Economics, University of Glasgow.
    40. Ewing, Bradley T. & Kang, Wensheng & Ratti, Ronald A., 2018. "The dynamic effects of oil supply shocks on the US stock market returns of upstream oil and gas companies," Energy Economics, Elsevier, vol. 72(C), pages 505-516.
    41. Nonejad, Nima, 2017. "Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 131-154.
    42. Eo Yunjong, 2016. "Structural changes in inflation dynamics: multiple breaks at different dates for different parameters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 211-231, June.
    43. Arnaud Dufays & Zhuo Li & Jeroen V.K. Rombouts & Yong Song, 2021. "Sparse change‐point VAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 703-727, September.
    44. Elena Afanasyeva, 2020. "Can Forecast Errors Predict Financial Crises? Exploring the Properties of a New Multivariate Credit Gap," Finance and Economics Discussion Series 2020-045, Board of Governors of the Federal Reserve System (U.S.).
    45. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2022. "Modelling the COVID‐19 infection trajectory: A piecewise linear quantile trend model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1589-1607, November.
    46. Franz Ruch & Mehmet Balcilar & Mampho P. Modise & Rangan Gupta, 2015. "Forecasting Core Inflation: The Case of South Africa," Working Papers 201543, University of Pretoria, Department of Economics.
    47. Eraslan, Sercan & Schröder, Maximilian, 2019. "Nowcasting GDP with a large factor model space," Discussion Papers 41/2019, Deutsche Bundesbank.
    48. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    49. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
    50. Augustyniak, Maciej & Dufays, Arnaud, 2018. "Modeling macroeconomic series with regime-switching models characterized by a high-dimensional state space," Economics Letters, Elsevier, vol. 170(C), pages 122-126.
    51. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2021. "Point and density forecasting of macroeconomic and financial uncertainties of the USA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 700-707, July.

  24. Korobilis, Dimitris, 2015. "Prior selection for panel vector autoregressions," SIRE Discussion Papers 2015-73, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Macroeconomic forecasting in a multi‐country context," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1230-1255, September.
    2. Christou, Christina & Cunado, Juncal & Gupta, Rangan & Hassapis, Christis, 2017. "Economic policy uncertainty and stock market returns in PacificRim countries: Evidence based on a Bayesian panel VAR model," Journal of Multinational Financial Management, Elsevier, vol. 40(C), pages 92-102.
    3. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    4. Koop, G & Korobilis, D, 2018. "Forecasting with High-Dimensional Panel VARs," Essex Finance Centre Working Papers 21329, University of Essex, Essex Business School.
    5. Christina Christou & Rangan Gupta & Christis Hassapis, 2016. "Does Economic Policy Uncertainty Forecast Real Housing Returns in a Panel of OECD Countries? A Bayesian Approach," Working Papers 201637, University of Pretoria, Department of Economics.
    6. Camehl, Annika & von Schweinitz, Gregor, 2023. "What explains international interest rate co-movement?," IWH Discussion Papers 3/2023, Halle Institute for Economic Research (IWH), revised 2023.
    7. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.
    8. Ibrahim Ayoade Adekunle & Sheriffdeen Adewale Tella & Oluwaseyi Adedayo Adelowokan, 2021. "Macroeconomic policy volatility and household consumption in Africa," SN Business & Economics, Springer, vol. 1(3), pages 1-22, March.
    9. Jesús Crespo Cuaresma & Martin Feldkircher & Florian Huber, 2016. "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1371-1391, November.
    10. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    11. Yang Aijun & Xiang Ju & Yang Hongqiang & Lin Jinguan, 2018. "Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1123-1138, April.
    12. Annika Schnücker, 2016. "Restrictions Search for Panel VARs," Discussion Papers of DIW Berlin 1612, DIW Berlin, German Institute for Economic Research.
    13. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    14. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2021. "Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs," Papers 2103.04944, arXiv.org, revised Feb 2022.
    15. Florian Huber & Tam'as Krisztin & Michael Pfarrhofer, 2018. "A Bayesian panel VAR model to analyze the impact of climate change on high-income economies," Papers 1804.01554, arXiv.org, revised Feb 2021.
    16. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
    17. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    18. Joscha Beckmann & Robert Czudaj, 2017. "Capital Flows and GDP in Emerging Economies and the Role of Global Spillovers," Chemnitz Economic Papers 009, Department of Economics, Chemnitz University of Technology, revised Jun 2017.
    19. Schnücker, A.M., 2019. "Penalized Estimation of Panel Vector Autoregressive Models," Econometric Institute Research Papers EI-2019-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    20. Antonio Pacifico, 2021. "Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," Econometrics, MDPI, vol. 9(2), pages 1-35, May.
    21. Schnücker, Annika, 2016. "Restrictions Search for Panel VARs," VfS Annual Conference 2016 (Augsburg): Demographic Change 145566, Verein für Socialpolitik / German Economic Association.
    22. Prüser, Jan & Blagov, Boris, 2022. "Improving inference and forecasting in VAR models using cross-sectional information," Ruhr Economic Papers 960, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

  25. Korobilis, Dimitris, 2014. "Data-based priors for vector autoregressions with drifting coefficients," SIRE Discussion Papers 2014-022, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Angelia L. Grant, 2017. "The Early Millennium Slowdown: Replicating the Peersman (2005) Results," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 224-232, January.
    2. Crespo Cuaresma, Jesús & Huber, Florian & Onorante, Luca, 2020. "Fragility and the effect of international uncertainty shocks," Journal of International Money and Finance, Elsevier, vol. 108(C).
    3. Nikolay Hristov & Oliver Hülsewig & Thomas Siemsen & Timo Wollmershäuser, 2019. "Restoring euro area monetary transmission: Which role for government bond rates?," Empirical Economics, Springer, vol. 57(3), pages 991-1021, September.
    4. Feldkircher, Martin & Kastner, Gregor & Huber, Florian, 2018. "Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?," Department of Economics Working Paper Series 260, WU Vienna University of Economics and Business.
    5. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    6. Pooyan Amir-Ahmadi & Christian Matthes & Mu-Chun Wang, 2020. "Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 124-136, January.
    7. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Apr 2023.
    8. Reusens Peter & Croux Christophe, 2017. "Detecting time variation in the price puzzle: a less informative prior choice for time varying parameter VAR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    9. Adam, Marc C. & Jansson, Walter, 2019. "Credit constraints and the propagation of the Great Depression in Germany," Discussion Papers 2019/12, Free University Berlin, School of Business & Economics.
    10. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    11. Belomestny, Denis & Krymova, Ekaterina & Polbin, Andrey, 2021. "Bayesian TVP-VARX models with time invariant long-run multipliers," Economic Modelling, Elsevier, vol. 101(C).
    12. Pooyan Amir-Ahmadi & Christian Matthes & Mu-Chun Wang, 2016. "Choosing Prior Hyperparameters," Working Paper 16-9, Federal Reserve Bank of Richmond.

  26. Joseph Byrne & Dimitris Korobilis & Pinho Ribeiro, 2014. "Exchange Rate Predictability in a Changing World," Papers 1403.0627, arXiv.org.

    Cited by:

    1. Teona Shugliashvili, 2023. "The words have power: the impact of news on exchange rates," FFA Working Papers 5.006, Prague University of Economics and Business, revised 31 Jul 2023.
    2. Demetrescu, Matei & Rodrigues, Paulo MM & Taylor, AM Robert, 2022. "Transformed Regression-based Long-Horizon Predictability Tests," Essex Finance Centre Working Papers 30620, University of Essex, Essex Business School.
    3. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
    4. de Souza Vasconcelos, Camila & Hadad Júnior, Eli, 2023. "Forecasting exchange rate: A bibliometric and content analysis," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 607-628.
    5. Florian Huber, 2017. "Structural breaks in Taylor rule based exchange rate models - Evidence from threshold time varying parameter models," Department of Economics Working Papers wuwp244, Vienna University of Economics and Business, Department of Economics.
    6. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    7. Panopoulou, Ekaterini & Souropanis, Ioannis, 2019. "The role of technical indicators in exchange rate forecasting," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 197-221.
    8. Liu, Li & Tan, Siming & Wang, Yudong, 2020. "Can commodity prices forecast exchange rates?," Energy Economics, Elsevier, vol. 87(C).
    9. Aristidou, Chrystalleni & Lee, Kevin & Shields, Kalvinder, 2022. "Fundamentals, regimes and exchange rate forecasts: Insights from a meta exchange rate model," Journal of International Money and Finance, Elsevier, vol. 123(C).
    10. Joseph Agyapong, 2021. "Application of Taylor Rule Fundamentals in Forecasting Exchange Rates," Economies, MDPI, vol. 9(2), pages 1-27, June.
    11. Huber, Florian & Zörner, Thomas, 2017. "Threshold cointegration and adaptive shrinkage," Department of Economics Working Paper Series 250, WU Vienna University of Economics and Business.
    12. Ibrahim D. Raheem & Xuan Vinh Vo, 2022. "A new approach to exchange rate forecast: The role of global financial cycle and time‐varying parameters," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2836-2848, July.
    13. Sercan Eraslan, 2019. "Asymmetric arbitrage trading on offshore and onshore renminbi markets," Empirical Economics, Springer, vol. 57(5), pages 1653-1675, November.
    14. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    15. Kharrat, Sabrine & Hammami, Yacine & Fatnassi, Ibrahim, 2020. "On the cross-sectional relation between exchange rates and future fundamentals," Economic Modelling, Elsevier, vol. 89(C), pages 484-501.
    16. Krystian Jaworski, 2021. "Forecasting exchange rates for Central and Eastern European currencies using country‐specific factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 977-999, September.
    17. Raheem, Ibrahim & Vo, Xuan Vinh, 2020. "A new approach to exchange rate forecast: The role of global financial cycle and time-varying parameters," MPRA Paper 105359, University Library of Munich, Germany.
    18. Beckmann, J & Koop, G & Korobilis, D & Schüssler, R, 2017. "Exchange rate predictability and dynamic Bayesian learning," Essex Finance Centre Working Papers 20781, University of Essex, Essex Business School.
    19. Raheem, Ibrahim, 2020. "Global financial cycles and exchange rate forecast: A factor analysis," MPRA Paper 105358, University Library of Munich, Germany.
    20. Florian, Huber & Kaufmann, Daniel, 2019. "Trend Fundamentals and Exchange Rate Dynamics," Working Papers in Economics 2019-4, University of Salzburg.
    21. Hauzenberger, Niko & Huber, Florian, 2018. "Model instability in predictive exchange rate regressions," Working Papers in Economics 2018-8, University of Salzburg.
    22. Alfredo Bateman y Javier E. Martinez & Javier Esteban Martinez, 2010. "Cuaderno 4: Análisis de las fuentes de oferta y demanda en el mercado de divisas," Cuadernos de Desarrollo Económico 7586, Secretaría Distrital de Desarrollo Económico.
    23. Michele Ca' Zorzi & Micha􏰀l Rubaszek, 2018. "Exchange rate forecasting on a napkin," GRU Working Paper Series GRU_2018_025, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    24. Afees A. Salisu & Rangan Gupta & Won Joong Kim, 2021. "Exchange Rate Predictability with Nine Alternative Models for BRICS Countries," Working Papers 202116, University of Pretoria, Department of Economics.
    25. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    26. Markus Hertrich, 2022. "Foreign exchange interventions under a minimum exchange rate regime and the Swiss franc," Review of International Economics, Wiley Blackwell, vol. 30(2), pages 450-489, May.
    27. Narayan, Paresh Kumar & Sharma, Susan Sunila & Phan, Dinh Hoang Bach & Liu, Guangqiang, 2020. "Predicting exchange rate returns," Emerging Markets Review, Elsevier, vol. 42(C).
    28. Frederik Kunze, 2020. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 313-333, March.
    29. Ojeda-Joya, Jair, 2019. "A consumption-based approach to exchange rate predictability," MPRA Paper 94231, University Library of Munich, Germany.
    30. Chen, Hongyi & Cao, Shuo, 2019. "Exchange Rate Movements and Fundamentals: Impact of Oil Prices and the People’s Republic of China’s Growth," ADBI Working Papers 938, Asian Development Bank Institute.
    31. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    32. Francesco Ravazzolo & Tommy Sveen & Sepideh K. Zahiri, 2016. "Commodity Futures and Forecasting Commodity Currencies," Working Papers No 7/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    33. Bruno P. C. Levy & Hedibert F. Lopes, 2021. "Dynamic Ordering Learning in Multivariate Forecasting," Papers 2101.04164, arXiv.org, revised Nov 2021.
    34. Kunze, Frederik, 2017. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," University of Göttingen Working Papers in Economics 326, University of Goettingen, Department of Economics.
    35. David Alaminos & M. Belén Salas & Manuel Á. Fernández-Gámez, 2023. "Quantum Monte Carlo simulations for estimating FOREX markets: a speculative attacks experience," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-21, December.
    36. Hertrich, Markus, 2020. "Foreign exchange interventions under a one-sided target zone regime and the Swiss franc," Discussion Papers 21/2020, Deutsche Bundesbank.
    37. Ren, Yu & Liang, Xuanxuan & Wang, Qin, 2021. "Short-term exchange rate forecasting: A panel combination approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    38. Zhang, Qian & Li, Zeguang, 2021. "Time-varying risk attitude and the foreign exchange market behavior," Research in International Business and Finance, Elsevier, vol. 57(C).
    39. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    40. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.

  27. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2014. "On the Sources of Uncertainty in Exchange Rate Predictability," SIRE Discussion Papers 2015-24, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Konstantin Styrin, 2018. "Forecasting inflation in Russia by Dynamic Model Averaging," Bank of Russia Working Paper Series wps39, Bank of Russia.
    2. Yin-Wong Cheung & Wenhao Wang, 2020. "Uncovered Interest Rate Parity Redux: Non- Uniform Effects," GRU Working Paper Series GRU_2020_004, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    3. de Souza Vasconcelos, Camila & Hadad Júnior, Eli, 2023. "Forecasting exchange rate: A bibliometric and content analysis," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 607-628.
    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. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    6. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    7. Panopoulou, Ekaterini & Souropanis, Ioannis, 2019. "The role of technical indicators in exchange rate forecasting," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 197-221.
    8. Yusupova, Alisa & Pavlidis, Nicos G. & Pavlidis, Efthymios G., 2023. "Dynamic linear models with adaptive discounting," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1925-1944.
    9. Sercan Eraslan, 2019. "Asymmetric arbitrage trading on offshore and onshore renminbi markets," Empirical Economics, Springer, vol. 57(5), pages 1653-1675, November.
    10. Kharrat, Sabrine & Hammami, Yacine & Fatnassi, Ibrahim, 2020. "On the cross-sectional relation between exchange rates and future fundamentals," Economic Modelling, Elsevier, vol. 89(C), pages 484-501.
    11. Ruipeng Liu & Riza Demirer & Rangan Gupta & Mark Wohar, 2020. "Volatility forecasting with bivariate multifractal models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 155-167, March.
    12. Adebayo Felix Adekoya & Isaac Kofi Nti & Benjamin Asubam Weyori, 2021. "Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi," FinTech, MDPI, vol. 1(1), pages 1-19, December.
    13. 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.
    14. Nima Zarrabi & Stuart Snaith & Jerry Coakley, 2022. "Exchange rate forecasting using economic models and technical trading rules," The European Journal of Finance, Taylor & Francis Journals, vol. 28(10), pages 997-1018, July.
    15. Alisa Yusupova & Nicos G. Pavlidis & Efthymios G. Pavlidis, 2019. "Adaptive Dynamic Model Averaging with an Application to House Price Forecasting," Papers 1912.04661, arXiv.org.
    16. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    17. Hauzenberger, Niko & Huber, Florian, 2018. "Model instability in predictive exchange rate regressions," Working Papers in Economics 2018-8, University of Salzburg.
    18. Byrne, Joseph P. & Ibrahim, Boulis Maher & Sakemoto, Ryuta, 2022. "The time-varying risk price of currency portfolios," Journal of International Money and Finance, Elsevier, vol. 124(C).
    19. Nima Nonejad, 2021. "Bayesian model averaging and the conditional volatility process: an application to predicting aggregate equity returns by conditioning on economic variables," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1387-1411, August.
    20. Sakemoto, Ryuta, 2019. "Currency carry trades and the conditional factor model," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 198-208.
    21. Eric Hillebrand & Jakob Guldbæk Mikkelsen & Lars Spreng & Giovanni Urga, 2023. "Exchange rates and macroeconomic fundamentals: Evidence of instabilities from time‐varying factor loadings," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 857-877, September.
    22. Afees A. Salisu & Rangan Gupta & Won Joong Kim, 2021. "Exchange Rate Predictability with Nine Alternative Models for BRICS Countries," Working Papers 202116, University of Pretoria, Department of Economics.
    23. Eraslan, Sercan & Schröder, Maximilian, 2023. "Nowcasting GDP with a pool of factor models and a fast estimation algorithm," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1460-1476.
    24. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    25. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. "“An application of deep learning for exchange rate forecasting”," AQR Working Papers 202201, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2022.
    26. Jan Prüser, 2019. "Adaptive learning from model space," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(1), pages 29-38, January.
    27. Chang, Ming-Jen & Matsuki, Takashi, 2022. "Exchange rate forecasting with real-time data: Evidence from Western offshoots," Research in International Business and Finance, Elsevier, vol. 59(C).
    28. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.
    29. Bruno P. C. Levy & Hedibert F. Lopes, 2021. "Dynamic Ordering Learning in Multivariate Forecasting," Papers 2101.04164, arXiv.org, revised Nov 2021.
    30. Pestova, Anna & Mamonov, Mikhail, 2019. "Should we care? The economic effects of financial sanctions on the Russian economy," BOFIT Discussion Papers 13/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
    31. Yin‐Wong Cheung & Shi He, 2022. "RMB misalignment: What does a meta‐analysis tell us?," Review of International Economics, Wiley Blackwell, vol. 30(4), pages 1038-1086, September.
    32. Borup, Daniel, 2019. "Asset pricing model uncertainty," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
    33. Mikhail Mamonov & Anna Pestova, 2021. ""Sorry, You're Blocked." Economic Effects of Financial Sanctions on the Russian Economy," CERGE-EI Working Papers wp704, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    34. Seojin Lee & Young Min Kim, 2020. "Effect of foreign exchange intervention: The case of Korea," Pacific Economic Review, Wiley Blackwell, vol. 25(5), pages 641-659, December.
    35. Ruipeng Liu & Riza Demirer & Rangan Gupta & Mark E. Wohar, 2017. "Do Bivariate Multifractal Models Improve Volatility Forecasting in Financial Time Series? An Application to Foreign Exchange and Stock Markets," Working Papers 201728, University of Pretoria, Department of Economics.
    36. Konstantin Styrin, 2019. "Forecasting Inflation in Russia Using Dynamic Model Averaging," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 3-18, March.
    37. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    38. Ribeiro, Pinho J., 2017. "Selecting exchange rate fundamentals by bootstrap," International Journal of Forecasting, Elsevier, vol. 33(4), pages 894-914.
    39. Byrne, Joseph P & Ibrahim, Boulis Maher & Sakemoto, Ryuta, 2017. "The Time-Varying Risk Price of Currency Carry Trades," MPRA Paper 80788, University Library of Munich, Germany.
    40. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.

  28. Koop, Gary & Korobilis, Dimitris, 2014. "Model Uncertainty in Panel Vector Autoregressive Models," SIRE Discussion Papers 2014-011, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Macroeconomic forecasting in a multi‐country context," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1230-1255, September.
    2. Fang Ye, 2023. "The New Development Bank and the structure of the multilateral development financial system," Review of Development Economics, Wiley Blackwell, vol. 27(3), pages 1957-1972, August.
    3. Anthony Orji & Jonathan E. Ogbuabor & Chiamaka F. Okolomike & Onyinye I. Anthony-Orji, 2022. "Do Capital Inflows and Financial Development, Influence Economic Growth in West Africa? Further Evidence from Transmission Mechanisms," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 7(1), pages 71-94.
    4. Christou, Christina & Cunado, Juncal & Gupta, Rangan & Hassapis, Christis, 2017. "Economic policy uncertainty and stock market returns in PacificRim countries: Evidence based on a Bayesian panel VAR model," Journal of Multinational Financial Management, Elsevier, vol. 40(C), pages 92-102.
    5. Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 81568, University Library of Munich, Germany.
    6. Koop, G & Korobilis, D, 2018. "Forecasting with High-Dimensional Panel VARs," Essex Finance Centre Working Papers 21329, University of Essex, Essex Business School.
    7. Christina Christou & Rangan Gupta & Christis Hassapis, 2016. "Does Economic Policy Uncertainty Forecast Real Housing Returns in a Panel of OECD Countries? A Bayesian Approach," Working Papers 201637, University of Pretoria, Department of Economics.
    8. Korobilis, Dimitris, 2015. "Prior selection for panel vector autoregressions," MPRA Paper 64143, University Library of Munich, Germany.
    9. Camehl, Annika & von Schweinitz, Gregor, 2023. "What explains international interest rate co-movement?," IWH Discussion Papers 3/2023, Halle Institute for Economic Research (IWH), revised 2023.
    10. Annalisa Marini & Steve McCorriston, 2017. "Propagation of Commodity Market Shocks," Discussion Papers 1708, University of Exeter, Department of Economics.
    11. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.
    12. Annika Camehl & Tomasz Wo'zniak, 2023. "Time-Varying Identification of Monetary Policy Shocks," Papers 2311.05883, arXiv.org, revised Nov 2023.
    13. Joshua C. C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2020. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 692-711, September.
    14. Florian, Huber & Kaufmann, Daniel, 2019. "Trend Fundamentals and Exchange Rate Dynamics," Working Papers in Economics 2019-4, University of Salzburg.
    15. Syarifuddin, Ferry, 2020. "Macroeconomic Consequences of Foreign Exchange Futures Market for Inflation Targeting Economies," MPRA Paper 104810, University Library of Munich, Germany.
    16. Beckmann, Joscha & Davidson, Sharada Nia & Koop, Gary & Schüssler, Rainer, 2023. "Cross-country uncertainty spillovers: Evidence from international survey data," Journal of International Money and Finance, Elsevier, vol. 130(C).
    17. Sharada Nia Davidson, 2022. "Regional Integration and Decoupling in the Asia Pacific: A Bayesian Panel VAR Approach," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(4), pages 773-807, December.
    18. Hazar Altınbaş & Vincenzo Pacelli & Edgardo Sica, 2022. "An Empirical Assessment of the Contagion Determinants in the Euro Area in a Period of Sovereign Debt Risk," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 8(2), pages 339-371, July.
    19. 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.
    20. Jesús Crespo Cuaresma & Martin Feldkircher & Florian Huber, 2016. "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1371-1391, November.
    21. Ters, Kristyna & Urban, Jörg, 2018. "Intraday dynamics of credit risk contagion before and during the euro area sovereign debt crisis: Evidence from central Europe," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 123-142.
    22. Thomas Goda & Santiago Sánchez González, 2024. "Export Market Size Matters: The Effect of the Market Size of Export Destinations on Manufacturing Growth," International Economic Journal, Taylor & Francis Journals, vol. 38(1), pages 21-44, January.
    23. Wang, Shengquan & Chen, Langnan & Xiong, Xiong, 2019. "Asset bubbles, banking stability and economic growth," Economic Modelling, Elsevier, vol. 78(C), pages 108-117.
    24. Matheus Koengkan & José Alberto Fuinhas, 2022. "The Interactions Between Renewable Energy Consumption, Economic Growth, and Globalisation: Fresh Evidence from the Mercosur Countries," Springer Books, in: Globalisation and Energy Transition in Latin America and the Caribbean, chapter 0, pages 63-99, Springer.
    25. Michelle, Gilmartin, 2016. "A note on the identification and transmission of energy demand and supply shocks," MPRA Paper 76186, University Library of Munich, Germany.
    26. Samuel F. Onipede & Nafiu A. Bashir & Jamaladeen Abubakar, 2023. "Small open economies and external shocks: an application of Bayesian global vector autoregression model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1673-1699, April.
    27. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-07, Economic Statistics Centre of Excellence (ESCoE).
    28. Marszk, Adam & Lechman, Ewa, 2019. "New technologies and diffusion of innovative financial products: Evidence on exchange-traded funds in selected emerging and developed economies," Journal of Macroeconomics, Elsevier, vol. 62(C).
    29. Hongbo Liu & Shuanglu Liang & Qingbo Cui, 2020. "The Nexus between Economic Complexity and Energy Consumption under the Context of Sustainable Environment: Evidence from the LMC Countries," IJERPH, MDPI, vol. 18(1), pages 1-14, December.
    30. Davidson, Sharada Nia, 2020. "Interdependence or contagion: A model switching approach with a focus on Latin America," Economic Modelling, Elsevier, vol. 85(C), pages 166-197.
    31. Renato Santiago & Matheus Koengkan & José Alberto Fuinhas & António Cardoso Marques, 2020. "The relationship between public capital stock, private capital stock and economic growth in the Latin American and Caribbean countries," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 67(3), pages 293-317, September.
    32. Karol Szafranek & Marek Kwas & Grzegorz Szafrański & Zuzanna Wośko, 2020. "Common Determinants of Credit Default Swap Premia in the North American Oil and Gas Industry. A Panel BMA Approach," Energies, MDPI, vol. 13(23), pages 1-23, November.
    33. Ozcan, Burcu & Tzeremes, Panayiotis G. & Tzeremes, Nickolaos G., 2020. "Energy consumption, economic growth and environmental degradation in OECD countries," Economic Modelling, Elsevier, vol. 84(C), pages 203-213.
    34. Huang, Yin-Siang & Chuang, Hui-Ching & Hasan, Iftekhar & Lin, Chih-Yung, 2021. "The effect of language on investing: Evidence from searches in Chinese versus English," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    35. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    36. Annika Schnücker, 2016. "Restrictions Search for Panel VARs," Discussion Papers of DIW Berlin 1612, DIW Berlin, German Institute for Economic Research.
    37. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    38. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2021. "Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs," Papers 2103.04944, arXiv.org, revised Feb 2022.
    39. Ferry Syarifuddin, 2020. "Macroeconomic Consequences Of Foreign Exchange Futures," Working Papers WP/14/2020, Bank Indonesia.
    40. Rasool Dehghanzadeh Shahabad & Mehmet Balcilar, 2022. "Modelling the Dynamic Interaction between Economic Policy Uncertainty and Commodity Prices in India: The Dynamic Autoregressive Distributed Lag Approach," Mathematics, MDPI, vol. 10(10), pages 1-21, May.
    41. Huang, Yingying & Duan, Kun & Mishra, Tapas, 2021. "Is Bitcoin really more than a diversifier? A pre- and post-COVID-19 analysis," Finance Research Letters, Elsevier, vol. 43(C).
    42. Florian Huber & Tam'as Krisztin & Michael Pfarrhofer, 2018. "A Bayesian panel VAR model to analyze the impact of climate change on high-income economies," Papers 1804.01554, arXiv.org, revised Feb 2021.
    43. Annalisa Marini, 2019. "The Impact of Weather on Commodity Prices: A Warning for the Future," Discussion Papers 1902, University of Exeter, Department of Economics.
    44. Roth, Markus, 2020. "Partial pooling with cross-country priors: An application to house price shocks," Discussion Papers 06/2020, Deutsche Bundesbank.
    45. Till Weigt & Bernd Wilfling, 2021. "An approach to increasing forecast‐combination accuracy through VAR error modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 686-699, July.
    46. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
    47. Lubos Komarek & Kristyna Ters, 2016. "Intraday dynamics of euro area sovereign credit risk contagion," BIS Working Papers 573, Bank for International Settlements.
    48. Schnücker, A.M., 2019. "Penalized Estimation of Panel Vector Autoregressive Models," Econometric Institute Research Papers EI-2019-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    49. Georgios Magkonis & Simon Rudkin, 2019. "Does Trilemma Speak Chinese?," Working Papers in Economics & Finance 2019-01, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    50. Antonio Pacifico, 2021. "Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," Econometrics, MDPI, vol. 9(2), pages 1-35, May.
    51. Liao, Jun & Zong, Xianpeng & Zhang, Xinyu & Zou, Guohua, 2019. "Model averaging based on leave-subject-out cross-validation for vector autoregressions," Journal of Econometrics, Elsevier, vol. 209(1), pages 35-60.
    52. Schnücker, Annika, 2016. "Restrictions Search for Panel VARs," VfS Annual Conference 2016 (Augsburg): Demographic Change 145566, Verein für Socialpolitik / German Economic Association.
    53. Prüser, Jan & Blagov, Boris, 2022. "Improving inference and forecasting in VAR models using cross-sectional information," Ruhr Economic Papers 960, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

  29. Gary, Koop & Dimitris, Korobilis, 2013. "A New Index of Financial Conditions," SIRE Discussion Papers 2013-48, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Sithole, Thanda & Simo-Kengne, Beatrice D. & Some, Modeste, 2017. "The role of financial conditions in transmitting external shocks to South Africa," International Economics, Elsevier, vol. 150(C), pages 36-56.
    2. Aharon, David Y. & Umar, Zaghum & Aziz, Mukhriz Izraf Azman & Vo, Xuan vinh, 2022. "COVID-19 related media sentiment and the yield curve of G-7 economies," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    3. Thibaut Duprey & Benjamin Klaus & Tuomas Peltonen, 2016. "Dating Systemic Financial Stress Episodes in the EU Countries," Staff Working Papers 16-11, Bank of Canada.
    4. Leu, Shawn C.-Y. & Robertson, Mari L., 2021. "Mortgage credit volumes and monetary policy after the Great Recession," Economic Modelling, Elsevier, vol. 94(C), pages 483-500.
    5. Tibor Szendrei & Katalin Varga, 2017. "FISS - A Factor Based Index of Systemic Stress in the Financial System," MNB Working Papers 2017/9, Magyar Nemzeti Bank (Central Bank of Hungary).
    6. Ioannis Chatziantoniou & David Gabauer, 2019. "EMU-Risk Synchronisation and Financial Fragility Through the Prism of Dynamic Connectedness," Working Papers in Economics & Finance 2019-07, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    7. Bouri, Elie & Cepni, Oguzhan & Gabauer, David & Gupta, Rangan, 2021. "Return connectedness across asset classes around the COVID-19 outbreak," International Review of Financial Analysis, Elsevier, vol. 73(C).
    8. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan, 2019. "International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression," International Review of Financial Analysis, Elsevier, vol. 65(C).
    9. Pham, Linh & Nguyen, Canh Phuc, 2022. "How do stock, oil, and economic policy uncertainty influence the green bond market?," Finance Research Letters, Elsevier, vol. 45(C).
    10. Darehshiri, Mahsa & Ghaemi Asl, Mahdi & Babatunde Adekoya, Oluwasegun & Shahzad, Umer, 2022. "Cross-spectral coherence and dynamic connectedness among contactless digital payments and digital communities, enterprise collaboration, and virtual reality firms," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    11. Bhargava, Apoorv & Gόrnicka, Lucyna & Xie, Peichu, 2023. "Leakages from macroprudential regulations: the case of household-specific tools and corporate credit," Working Paper Series 2784, European Central Bank.
    12. Chandrarin, Grahita & Sohag, Kazi & Cahyaningsih, Diyah Sukanti & Yuniawan, Dani & Herdhayinta, Heyvon, 2022. "The response of exchange rate to coal price, palm oil price, and inflation in Indonesia: Tail dependence analysis," Resources Policy, Elsevier, vol. 77(C).
    13. Arrigoni, Simone & Bobasu, Alina & Venditti, Fabrizio, 2021. "The simpler, the better: Measuring financial conditions for monetary policy and financial stability," EIB Working Papers 2021/10, European Investment Bank (EIB).
    14. Somnath Chatterjee & Marea Sing, 2021. "Measuring Systemic Risk in South African Banks," Working Papers 11004, South African Reserve Bank.
    15. Sohag, Kazi & Hassan, M. Kabir & Kalina, Irina & Mariev, Oleg, 2023. "The relative response of Russian National Wealth Fund to oil demand, supply and risk shocks," Energy Economics, Elsevier, vol. 123(C).
    16. Julián Andrada-Félix & Adrian Fernandez-Perez & Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero, 2022. "Time connectedness of fear," Empirical Economics, Springer, vol. 62(3), pages 905-931, March.
      • Julián Andrada-Félixa & Adrian Fernandez-Perez & Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero, 2018. "“Time connectedness of fear”," IREA Working Papers 201818, University of Barcelona, Research Institute of Applied Economics, revised Sep 2018.
    17. Gian Paulo Soave, 2023. "A panel threshold VAR with stochastic volatility-in-mean model: an application to the effects of financial and uncertainty shocks in emerging economies," Applied Economics, Taylor & Francis Journals, vol. 55(4), pages 397-431, January.
    18. Dimitris Korobilis & Maximilian Schröder, 2023. "Monitoring multicountry macroeconomic risk," Working Papers No 06/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    19. Fabrizio Ferriani & Andrea Gazzani, 2021. "Financial condition indices for emerging market economies: can Google help?," Questioni di Economia e Finanza (Occasional Papers) 653, Bank of Italy, Economic Research and International Relations Area.
    20. Feng, Huiqun & Zhang, Jun & Guo, Na, 2023. "Time-varying linkages between energy and stock markets: Dynamic spillovers and driving factors," International Review of Financial Analysis, Elsevier, vol. 89(C).
    21. Alain Kabundi & Asithandile Mbelu, 2021. "Estimating a time-varying financial conditions index for South Africa," Empirical Economics, Springer, vol. 60(4), pages 1817-1844, April.
    22. 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.
    23. Kappler, Marcus & Schleer, Frauke, 2017. "A financially stressed euro area," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 11, pages 1-37.
    24. Tomas Adam & Miroslav Plasil, 2014. "The Impact of Financial Variables on Czech Macroeconomic Developments: An Empirical Investigation," Working Papers 2014/11, Czech National Bank.
    25. Urom, Christian & Mzoughi, Hela & Ndubuisi, Gideon & Guesmi, Khaled, 2022. "Directional predictability and time-frequency spillovers among clean energy sectors and oil price uncertainty," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 326-341.
    26. Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2015. "Forecasting with VAR Models: Fat Tails and Stochastic Volatility," CReMFi Discussion Papers 2, CReMFi, School of Economics and Finance, QMUL.
    27. Michael T. Kiley, 2020. "Financial Conditions and Economic Activity: Insights from Machine Learning," Finance and Economics Discussion Series 2020-095, Board of Governors of the Federal Reserve System (U.S.).
    28. Ana Beatriz Galvão & Michael T. Owyang, 2018. "Financial Stress Regimes and the Macroeconomy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(7), pages 1479-1505, October.
    29. Risse, Marian & Ohl, Ludwig, 2017. "Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 158-176.
    30. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2019. "Decomposing global yield curve co-movement," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 500-513.
    31. Capasso Salvatore & Oreste Napolitano & Ana Laura Vivero, 2023. "The Financial Conditions Index as an additional tool for policymakers in developing countries: the Mexican case," CSEF Working Papers 664, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    32. Gian Paulo Soave, 2016. "Choques Fiscais E Instabilidade Financeira No Brasil: Uma Abordagem Tvar," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 045, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    33. Zhu, Sheng & Kavanagh, Ella & O’Sullivan, Niall, 2021. "Inflation targeting and financial conditions: UK monetary policy during the great moderation and financial crisis," Journal of Financial Stability, Elsevier, vol. 53(C).
    34. Lin Zhu & Jian He, 2024. "China financial stability and asymmetric implications for economic stability," Economic Change and Restructuring, Springer, vol. 57(1), pages 1-29, February.
    35. Agyei, Samuel Kwaku & Umar, Zaghum & Bossman, Ahmed & Teplova, Tamara, 2023. "Dynamic connectedness between global commodity sectors, news sentiment, and sub-Saharan African equities," Emerging Markets Review, Elsevier, vol. 56(C).
    36. Ductor, Lorenzo & Leiva-Leon, Danilo, 2016. "Dynamics of global business cycle interdependence," Journal of International Economics, Elsevier, vol. 102(C), pages 110-127.
    37. Akyildirim, Erdinc & Cepni, Oguzhan & Molnár, Peter & Uddin, Gazi Salah, 2022. "Connectedness of energy markets around the world during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 109(C).
    38. Ali, Shoaib & Ijaz, Muhammad Shahzad & Yousaf, Imran, 2023. "Dynamic spillovers and portfolio risk management between defi and metals: Empirical evidence from the Covid-19," Resources Policy, Elsevier, vol. 83(C).
    39. Poncela Blanco, Maria Pilar & Ruiz Ortega, Esther & Miranda Gualdrón, Karen Alejandra, 2020. "Factor extraction using Kalman filter and smoothing: this is not just another survey," DES - Working Papers. Statistics and Econometrics. WS 30644, Universidad Carlos III de Madrid. Departamento de Estadística.
    40. Jun Gao & Sheng Zhu & Niall O’Sullivan & Meadhbh Sherman, 2019. "The Role of Economic Uncertainty in UK Stock Returns," JRFM, MDPI, vol. 12(1), pages 1-16, January.
    41. Sungurtekin Hallam, Bahar, 2022. "Emerging market responses to external shocks: A cross-country analysis," Economic Modelling, Elsevier, vol. 115(C).
    42. Umar, Zaghum & Mokni, Khaled & Escribano, Ana, 2022. "Connectedness between the COVID-19 related media coverage and Islamic equities: The role of economic policy uncertainty," Pacific-Basin Finance Journal, Elsevier, vol. 75(C).
    43. Margarita Debuque-Gonzales & Maria Socorro Gochoco-Bautista, 2017. "Financial Conditions Indexes and Monetary Policy in Asia," Asian Economic Papers, MIT Press, vol. 16(2), pages 83-117, Summer.
    44. Abiodun Moses Adetokunbo & Afe Success Mevhare, 2024. "The interconnectivity between green stocks, oil prices, and uncertainty surrounding economic policy: indications from the United States," SN Business & Economics, Springer, vol. 4(2), pages 1-26, February.
    45. Yanhong Feng & Dilong Xu & Pierre Failler & Tinghui Li, 2020. "Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation," Sustainability, MDPI, vol. 12(16), pages 1-24, August.
    46. Yunhan Zhang & Qiang Ji & David Gabauer & Rangan Gupta, 2024. "How Connected is the Oil-Bank Network? Firm-Level and High-Frequency Evidence," Working Papers 202405, University of Pretoria, Department of Economics.
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    51. Chen, Zhengyang & Valcarcel, Victor J., 2021. "Monetary transmission in money markets: The not-so-elusive missing piece of the puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
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    54. Eguren-Martin, Fernando & O’Neill, Cian & Sokol, Andrej & Berge, Lukas von dem, 2021. "Capital flows-at-risk: push, pull and the role of policy," Working Paper Series 2538, European Central Bank.
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    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2016. "Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 15-084/III, Tinbergen Institute, revised 03 Jul 2017.
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    4. Ioannis Chatziantoniou & David Gabauer, 2019. "EMU-Risk Synchronisation and Financial Fragility Through the Prism of Dynamic Connectedness," Working Papers in Economics & Finance 2019-07, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
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    6. Angelia L. Grant, 2017. "The Early Millennium Slowdown: Replicating the Peersman (2005) Results," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 224-232, January.
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    223. María Dolores Gadea-Rivas & Ana Gómez-Loscos & Danilo Leiva-Leon, 2017. "The evolution of regional economic interlinkages in Europe," Working Papers 1705, Banco de España.
    224. Niall O’Sullivan & Sheng Zhu & Jason Foran, 2019. "Sentiment versus liquidity pricing effects in the cross-section of UK stock returns," Journal of Asset Management, Palgrave Macmillan, vol. 20(4), pages 317-329, July.
    225. Joshua C.C. Chan & Eric Eisenstat, 2018. "Comparing hybrid time-varying parameter VARs," CAMA Working Papers 2018-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    226. Lyu, Chenyan & Scholtens, Bert, 2022. "Is the Global Carbon Market Integrated? Return and Volatility Connectedness in ETS Systems," Working Papers 7-2022, Copenhagen Business School, Department of Economics, revised 08 Jun 2022.
    227. Belomestny, Denis & Krymova, Ekaterina & Polbin, Andrey, 2021. "Bayesian TVP-VARX models with time invariant long-run multipliers," Economic Modelling, Elsevier, vol. 101(C).
    228. Denis Belomestny & Ekaterina Krymova & Andrey Polbin, 2020. "Estimating TVP-VAR models with time invariant long-run multipliers," Papers 2008.00718, arXiv.org.
    229. Liew, Ping-Xin & Lim, Kian-Ping & Goh, Kim-Leng, 2022. "The dynamics and determinants of liquidity connectedness across financial asset markets," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 341-358.
    230. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Equity Premia using Bayesian Dynamic Model Averaging," CQE Working Papers 2914, Center for Quantitative Economics (CQE), University of Muenster.
    231. Kenwin Maung, 2021. "Estimating high-dimensional Markov-switching VARs," Papers 2107.12552, arXiv.org.
    232. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
    233. Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.
    234. McAlinn, Kenichiro & West, Mike, 2019. "Dynamic Bayesian predictive synthesis in time series forecasting," Journal of Econometrics, Elsevier, vol. 210(1), pages 155-169.
    235. Spyros Makridakis & Andreas Merikas & Anna Merika & Mike G. Tsionas & Marwan Izzeldin, 2020. "A novel forecasting model for the Baltic dry index utilizing optimal squeezing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 56-68, January.
    236. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.

  31. Korobilis, Dimitris, 2012. "Bayesian forecasting with highly correlated predictors," SIRE Discussion Papers 2012-80, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Dimitris, Korobilis, 2013. "Forecasting with Factor Models: A Bayesian Model Averaging Perspective," MPRA Paper 52724, University Library of Munich, Germany.
    2. Omokolade Akinsomi & Goodness C. Aye & Vassilios Babalos & Fotini Economou & Rangan Gupta, 2016. "Real estate returns predictability revisited: novel evidence from the US REITs market," Empirical Economics, Springer, vol. 51(3), pages 1165-1190, November.
    3. Konstantakis, Konstantinos N. & Michaelides, Panayotis G., 2014. "Transmission of the debt crisis: From EU15 to USA or vice versa? A GVAR approach," Journal of Economics and Business, Elsevier, vol. 76(C), pages 115-132.
    4. Paul Hofmarcher & Jesús Crespo Cuaresma & Bettina Grün & Kurt Hornik, 2015. "Last Night a Shrinkage Saved My Life: Economic Growth, Model Uncertainty and Correlated Regressors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 133-144, March.
    5. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2013. "Forecasting the US Real Private Residential Fixed Investment Using Large Number of Predictors," Working Papers 201348, University of Pretoria, Department of Economics.
    6. Gary Koop, 2013. "Using VARs and TVP-VARs with Many Macroeconomic Variables," Working Papers 1303, University of Strathclyde Business School, Department of Economics.
    7. Dimitris Korobilis, 2014. "Data-based priors for vector autoregressions with drifting coefficients," Working Papers 2014_04, Business School - Economics, University of Glasgow.
    8. Konstantakis, Konstantinos & Michaelides, Panayotis G., 2014. "Combining Input-Output (IO) analysis with Global Vector Autoregressive (GVAR) modeling: Evidence for the USA (1992-2006)," MPRA Paper 67111, University Library of Munich, Germany.
    9. Ramazan EKİNCİ & Osman TÜZÜN & Fatih CEYLAN & Hakan KAHYAOĞLU, 2017. "Dışa Açıklık ile İşsizlik Arasındaki İlişki: Seçilmiş AB Ülkeleri ve Türkiye Üzerine Zamana Göre Değişen Parametreli Bir Analiz Algıları," Sosyoekonomi Journal, Sosyoekonomi Society, issue 25(31).
    10. Nombulelo Gumata & Alain Kabundi & Eliphas Ndou, 2013. "Important channels of transmission of monetary policy shock in South Africa," Working Papers 6021, South African Reserve Bank.
    11. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "Heterogeneous variable selection in nonlinear panel data models: A semiparametric Bayesian approach," Tinbergen Institute Discussion Papers 20-061/III, Tinbergen Institute.
    12. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    13. Naik, Prasad A., 2015. "Marketing Dynamics: A Primer on Estimation and Control," Foundations and Trends(R) in Marketing, now publishers, vol. 9(3), pages 175-266, December.
    14. Kuo-Jung Lee & Yi-Chi Chen, 2018. "Of needles and haystacks: revisiting growth determinants by robust Bayesian variable selection," Empirical Economics, Springer, vol. 54(4), pages 1517-1547, June.
    15. Goodness C. Aye & Rangan Gupta, 2013. "Forecasting Real House Price of the U.S.: An Analysis Covering 1890 to 2012," Working Papers 201362, University of Pretoria, Department of Economics.
    16. Nicholas Apergis & Ghassen El Montasser & Emmanuel Owusu-Sekyere & Ahdi N. Ajmi & Rangan Gupta, 2014. "Dutch Disease Effect of Oil Rents on Agriculture Value Added in MENA Countries," Working Papers 201408, University of Pretoria, Department of Economics.
    17. Yang Aijun & Xiang Ju & Yang Hongqiang & Lin Jinguan, 2018. "Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1123-1138, April.
    18. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    19. Malefaki, Valia, 2015. "On Flexible Linear Factor Stochastic Volatility Models," MPRA Paper 62216, University Library of Munich, Germany.

  32. KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," LIDAM Discussion Papers CORE 2011021, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    2. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    3. Ramis Khabibullin, 2019. "What measures of real economic activity slack are helpful for forecasting Russian inflation?," Bank of Russia Working Paper Series wps50, Bank of Russia.
    4. Jiahan Li & Ilias Tsiakas & Wei Wang, 2015. "Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 293-341.
    5. Jan Prüser, 2019. "Forecasting with many predictors using Bayesian additive regression trees," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(7), pages 621-631, November.
    6. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
    7. Desbordes, Rodolphe & Koop, Gary & Vicard, Vincent, 2018. "One size does not fit all… panel data: Bayesian model averaging and data poolability," Economic Modelling, Elsevier, vol. 75(C), pages 364-376.
    8. 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.
    9. Gregor Kastner, 2016. "Sparse Bayesian time-varying covariance estimation in many dimensions," Papers 1608.08468, arXiv.org, revised Nov 2017.
    10. Dimitris Korobilis & Davide Pettenuzzo, 2018. "Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions," Working Paper series 18-21, Rimini Centre for Economic Analysis.
    11. Dimitris, Korobilis, 2013. "Forecasting with Factor Models: A Bayesian Model Averaging Perspective," MPRA Paper 52724, University Library of Munich, Germany.
    12. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2013. "Forecasting the US Real Private Residential Fixed Investment Using Large Number of Predictors," Working Papers 201348, University of Pretoria, Department of Economics.
    13. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
    14. Feldkircher, Martin & Kastner, Gregor & Huber, Florian, 2018. "Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?," Department of Economics Working Paper Series 260, WU Vienna University of Economics and Business.
    15. Rangan Gupta & Mampho P. Modise & Josine Uwilingiye, 2011. "Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors," Working Papers 201122, University of Pretoria, Department of Economics.
    16. Dimitris Korobilis, 2014. "Data-based priors for vector autoregressions with drifting coefficients," Working Papers 2014_04, Business School - Economics, University of Glasgow.
    17. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," University of California at San Diego, Economics Working Paper Series qt1st3n7z7, Department of Economics, UC San Diego.
    18. Vasilios Plakandaras & Rangan Gupta & Periklis Gogas & Theophilos Papadimitriou, 2017. "Forecasting the U.S. Real House Price Index," Papers 1707.04868, arXiv.org.
    19. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    20. Sandra Stankiewicz, 2015. "Forecasting Euro Area Macroeconomic Variables with Bayesian Adaptive Elastic Net," Working Paper Series of the Department of Economics, University of Konstanz 2015-12, Department of Economics, University of Konstanz.
    21. Frédérique Bec & Matteo Mogliani, 2013. "Nowcasting French GDP in Real-Time from Survey Opinions : Information or Forecast Combinations ?," Working Papers 2013-21, Center for Research in Economics and Statistics.
    22. Jan Prüser, 2021. "Forecasting US inflation using Markov dimension switching," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 481-499, April.
    23. Nombulelo Gumata & Alain Kabundi & Eliphas Ndou, 2013. "Important channels of transmission of monetary policy shock in South Africa," Working Papers 6021, South African Reserve Bank.
    24. Follett, Lendie & Yu, Cindy, 2019. "Achieving parsimony in Bayesian vector autoregressions with the horseshoe prior," Econometrics and Statistics, Elsevier, vol. 11(C), pages 130-144.
    25. Michelle, Gilmartin, 2016. "A note on the identification and transmission of energy demand and supply shocks," MPRA Paper 76186, University Library of Munich, Germany.
    26. Naik, Prasad A., 2015. "Marketing Dynamics: A Primer on Estimation and Control," Foundations and Trends(R) in Marketing, now publishers, vol. 9(3), pages 175-266, December.
    27. Korobilis, D, 2017. "Forecasting with many predictors using message passing algorithms," Essex Finance Centre Working Papers 19565, University of Essex, Essex Business School.
    28. Nicholas Apergis & Ghassen El Montasser & Emmanuel Owusu-Sekyere & Ahdi N. Ajmi & Rangan Gupta, 2014. "Dutch Disease Effect of Oil Rents on Agriculture Value Added in MENA Countries," Working Papers 201408, University of Pretoria, Department of Economics.
    29. Goodness C. Aye & Rangan Gupta, 2013. "Forecasting Real House Price of the U.S.: An Analysis Covering 1890 to 2012," Working Papers 201362, University of Pretoria, Department of Economics.
    30. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    31. Gefang, Deborah, 2014. "Bayesian doubly adaptive elastic-net Lasso for VAR shrinkage," International Journal of Forecasting, Elsevier, vol. 30(1), pages 1-11.
    32. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    33. Dimitris Korobilis, 2019. "High-dimensional macroeconomic forecasting using message passing algorithms," Working Papers 2019_07, Business School - Economics, University of Glasgow.
    34. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    35. Yang Aijun & Xiang Ju & Yang Hongqiang & Lin Jinguan, 2018. "Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1123-1138, April.
    36. Vasilios Plakandaras & Rangan Gupta & Constantinos Katrakilidis & Mark E. Wohar, 2017. "Time-Varying Role of Macroeconomic Shocks on House Prices in the US and UK: Evidence from Over 150 Years of Data," Working Papers 201765, University of Pretoria, Department of Economics.
    37. Matei, Florin, 2014. "An empirical examination of stock market integration in EMU," MPRA Paper 60717, University Library of Munich, Germany.
    38. Roberto Casarin & Daniel Felix Ahelegbey & Monica Billio, 2014. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Working Papers 2014:29, Department of Economics, University of Venice "Ca' Foscari".
    39. Huang, Y-F., 2012. "Forecasting Chinese inflation and output: A Bayesian vector autoregressive approach," MPRA Paper 41933, University Library of Munich, Germany.
    40. Prüser, Jan, 2017. "Forecasting US inflation using Markov dimension switching," Ruhr Economic Papers 710, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    41. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
    42. Malefaki, Valia, 2015. "On Flexible Linear Factor Stochastic Volatility Models," MPRA Paper 62216, University Library of Munich, Germany.

  33. BAUWENS, Luc & KOROBILIS, Dimitris, 2011. "Bayesian methods," LIDAM Discussion Papers CORE 2011061, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Vouldis, Angelos, 2015. "Credit market disequilibrium in Greece (2003-2011) - a Bayesian approach," Working Paper Series 1805, European Central Bank.

  34. KOROBILIS, Dimitris, 2011. "VAR forecasting using Bayesian variable selection," LIDAM Discussion Papers CORE 2011022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Tsionas, Efthymios G. & Konstantakis, Konstantinos N. & Michaelides, Panayotis G., 2016. "Bayesian GVAR with k-endogenous dominants & input–output weights: Financial and trade channels in crisis transmission for BRICs," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 42(C), pages 1-26.
    2. SENBETA, Sisay Regassa, 2012. "How important are external shocks in explaining growth in Sub-Saharan Africa? Evidence from a Bayesian VAR," Working Papers 2012010, University of Antwerp, Faculty of Business and Economics.
    3. Joshua C.C. Chan & Eric Eisenstat & Gary Koop, 2014. "Large Bayesian VARMAs," Working Paper series 40_14, Rimini Centre for Economic Analysis.
    4. 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.
    5. Mehmet Balcilar & Nico Katzke & Rangan Gupta, 2015. "Do Precious Metal Prices Help in Forecasting South African Inflation?," Working Papers 03/2015, Stellenbosch University, Department of Economics.
    6. Silvia Miranda Agrippino & Giovanni Ricco, 2018. "Bayesian vector autoregressions," SciencePo Working papers Main hal-03458277, HAL.
    7. Koop, Gary & Korobilis, Dimitris, 2016. "Model uncertainty in Panel Vector Autoregressive models," European Economic Review, Elsevier, vol. 81(C), pages 115-131.
    8. Zhe Yu & Raquel Prado & Erin Burke Quinlan & Steven C. Cramer & Hernando Ombao, 2016. "Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 549-563, April.
    9. Dimitris Korobilis & Davide Pettenuzzo, 2018. "Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions," Working Paper series 18-21, Rimini Centre for Economic Analysis.
    10. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
    11. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working papers 2012-38, University of Connecticut, Department of Economics, revised Dec 2013.
    12. Dimitris, Korobilis, 2013. "Forecasting with Factor Models: A Bayesian Model Averaging Perspective," MPRA Paper 52724, University Library of Munich, Germany.
    13. Erlan Konebayev, 2023. "Forecasting a Commodity-Exporting Small Open Developing Economy Using DSGE and DSGE-BVAR," International Economic Journal, Taylor & Francis Journals, vol. 37(1), pages 39-70, January.
    14. Joseph Byrne & Dimitris Korobilis & Pinho Ribeiro, 2014. "Exchange Rate Predictability in a Changing World," Papers 1403.0627, arXiv.org.
    15. Zhou, Xiaocong & Nakajima, Jouchi & West, Mike, 2014. "Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 963-980.
    16. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Business School.
    17. Gary Koop, 2013. "Using VARs and TVP-VARs with Many Macroeconomic Variables," Working Papers 1303, University of Strathclyde Business School, Department of Economics.
    18. 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.
    19. Michaelides, Panayotis G. & Tsionas, Efthymios G. & Konstantakis, Konstantinos N., 2018. "Debt dynamics in Europe: a network general equilibrium GVAR approach," LSE Research Online Documents on Economics 86865, London School of Economics and Political Science, LSE Library.
    20. Florian Eckert & Nina Mühlebach, 2021. "Global and Local Components of Output Gaps," KOF Working papers 21-497, KOF Swiss Economic Institute, ETH Zurich.
    21. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
    22. GABSZEWICZ, Jean & TAROLA, Ornella, 2011. "Migration, wage differentials and fiscal competition," LIDAM Discussion Papers CORE 2011065, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    23. Korobilis, Dimitris, 2013. "Bayesian forecasting with highly correlated predictors," Economics Letters, Elsevier, vol. 118(1), pages 148-150.
    24. Korobilis, Dimitris, 2015. "Prior selection for panel vector autoregressions," MPRA Paper 64143, University Library of Munich, Germany.
    25. Zeyyad Mandalinci, 2015. "Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models," CReMFi Discussion Papers 3, CReMFi, School of Economics and Finance, QMUL.
    26. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
    27. Gary Koop & Dimitris Korobilis, 2009. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Working Paper series 47_09, Rimini Centre for Economic Analysis.
    28. Gary Koop & Dimitris Korobilis, 2012. "Large Time-Varying Parameter VARs," Working Paper series 11_12, Rimini Centre for Economic Analysis.
    29. Chan, Joshua C.C. & Eisenstat, Eric & Strachan, Rodney W., 2020. "Reducing the state space dimension in a large TVP-VAR," Journal of Econometrics, Elsevier, vol. 218(1), pages 105-118.
    30. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo Matlab Toolbox," Working Papers 2013:08, Department of Economics, University of Venice "Ca' Foscari".
    31. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    32. Joshua C.C. Chan & Gary Koop & Roberto Leon-Gonzalez & Rodney W. Strachan, 2010. "Time Varying Dimension Models," Working Paper series 44_10, Rimini Centre for Economic Analysis.
    33. Laséen, Stefan & Strid, Ingvar, 2013. "Debt Dynamics and Monetary Policy: A Note," Working Paper Series 283, Sveriges Riksbank (Central Bank of Sweden).
    34. Mehmet Balcilar & Rangan Gupta & Kevin Kotze, 2013. "Forecasting South African Macroeconomic Data with a Nonlinear DSGE Model," Working Papers 201313, University of Pretoria, Department of Economics.
    35. Petre Caraiani, 2014. "Do money and financial variables help forecasting output in emerging European Economies?," Empirical Economics, Springer, vol. 46(2), pages 743-763, March.
    36. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2020. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1092-1110, July.
    37. Nataliia Ostapenko, 2022. "Do output gap estimates improve inflation forecasts in Slovakia?," Working and Discussion Papers WP 4/2022, Research Department, National Bank of Slovakia.
    38. Dimitris Korobilis, 2014. "Data-based priors for vector autoregressions with drifting coefficients," Working Papers 2014_04, Business School - Economics, University of Glasgow.
    39. Matkovskyy, Roman, 2012. "The Index of the Financial Safety (IFS) of South Africa and Bayesian Estimates for IFS Vector-Autoregressive Model," MPRA Paper 42173, University Library of Munich, Germany.
    40. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.
    41. Louzis Dimitrios P., 2016. "Steady-state priors and Bayesian variable selection in VAR forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(5), pages 495-527, December.
    42. Joshua C. C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2020. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 692-711, September.
    43. Jouchi Nakajima & Mike West, 2013. "Bayesian Analysis of Latent Threshold Dynamic Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 151-164, April.
    44. Irfan akbar Kazi & Hakimzadi Wagan & Farhan Akbar, 2011. "The changing international transmission of us monetary policy shocks: is there evidence of contagion effect on oecd countries," Economics Bulletin, AccessEcon, vol. 31(4), pages 1-49.
    45. Sebastian Ankargren & Mårten Bjellerup & Hovick Shahnazarian, 2017. "The importance of the financial system for the real economy," Empirical Economics, Springer, vol. 53(4), pages 1553-1586, December.
    46. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse seemingly unrelated regression model (SUR)," Working Papers 2016:20, Department of Economics, University of Venice "Ca' Foscari".
    47. Michaelides, Panayotis G. & Tsionas, Efthymios G. & Konstantakis, Konstantinos N., 2018. "Debt Crisis in Europe (2001-2015): A Network General Equilibrium GVAR approach," MPRA Paper 89998, University Library of Munich, Germany.
    48. Gil-Alana, Luis A. & Gupta, Rangan & Olubusoye, Olusanya E. & Yaya, OlaOluwa S., 2016. "Time series analysis of persistence in crude oil price volatility across bull and bear regimes," Energy, Elsevier, vol. 109(C), pages 29-37.
    49. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper series 18-37, Rimini Centre for Economic Analysis.
    50. Jan Prüser, 2021. "Forecasting US inflation using Markov dimension switching," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 481-499, April.
    51. Eric Eisenstat & Joshua C.C. Chan & Rodney Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," Working Paper series 44_14, Rimini Centre for Economic Analysis.
    52. Ramazan EKİNCİ & Osman TÜZÜN & Fatih CEYLAN & Hakan KAHYAOĞLU, 2017. "Dışa Açıklık ile İşsizlik Arasındaki İlişki: Seçilmiş AB Ülkeleri ve Türkiye Üzerine Zamana Göre Değişen Parametreli Bir Analiz Algıları," Sosyoekonomi Journal, Sosyoekonomi Society, issue 25(31).
    53. 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.
    54. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2022. "Using hierarchical aggregation constraints to nowcast regional economic aggregates," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-04, Economic Statistics Centre of Excellence (ESCoE).
    55. Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
    56. D. Tutberidze & D. Japaridze, 2017. "Macroeconomic Forecasting Using Bayesian Vector Autoregressive Approach," Вестник Киевского национального университета имени Тараса Шевченко. Экономика., Socionet;Киевский национальный университет имени Тараса Шевченко, vol. 2(191), pages 42-49.
    57. Follett, Lendie & Yu, Cindy, 2019. "Achieving parsimony in Bayesian vector autoregressions with the horseshoe prior," Econometrics and Statistics, Elsevier, vol. 11(C), pages 130-144.
    58. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    59. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    60. Michelle, Gilmartin, 2016. "A note on the identification and transmission of energy demand and supply shocks," MPRA Paper 76186, University Library of Munich, Germany.
    61. Samuel F. Onipede & Nafiu A. Bashir & Jamaladeen Abubakar, 2023. "Small open economies and external shocks: an application of Bayesian global vector autoregression model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1673-1699, April.
    62. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-07, Economic Statistics Centre of Excellence (ESCoE).
    63. Florian Eckert & Nina Mühlebach, 2023. "Global and local components of output gaps," Empirical Economics, Springer, vol. 65(5), pages 2301-2331, November.
    64. Daniel R. Kowal & David S. Matteson & David Ruppert, 2019. "Functional Autoregression for Sparsely Sampled Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 97-109, January.
    65. Matkovskyy, Roman, 2012. "Прогнозування розвитку економіки України на основі баєсівських авторегресійних (BVAR) моделей з різними priors [Forecasting Economic Development of Ukraine based on BVAR models with different prior," MPRA Paper 44725, University Library of Munich, Germany, revised Nov 2012.
    66. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    67. Joshua C C Chan, 2012. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," ANU Working Papers in Economics and Econometrics 2012-591, Australian National University, College of Business and Economics, School of Economics.
    68. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    69. Inske Pirschel & Maik H. Wolters, 2018. "Forecasting with large datasets: compressing information before, during or after the estimation?," Empirical Economics, Springer, vol. 55(2), pages 573-596, September.
    70. Smith, Simon C. & Timmermann, Allan & Zhu, Yinchu, 2019. "Variable selection in panel models with breaks," Journal of Econometrics, Elsevier, vol. 212(1), pages 323-344.
    71. Mihaela Simionescu, 2016. "Foreign Direct Investment and Sustainable Development. A Regional Approach for Romania," Working Papers of Macroeconomic Modelling Seminar 162702, Institute for Economic Forecasting.
    72. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    73. Kim, Young Min & Lee, Seojin, 2020. "Exchange rate predictability: A variable selection perspective," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 117-134.
    74. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    75. Luo, Jiawen & Klein, Tony & Walther, Thomas & Ji, Qiang, 2021. "Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning," QBS Working Paper Series 2021/04, Queen's University Belfast, Queen's Business School.
    76. Joshua Chan & Luca Benati & Eric Eisenstat & Gary Koop, 2018. "Identifying Noise Shocks," Working Paper Series 41, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    77. Aijun Yang & Ju Xiang & Lianjie Shu & Hongqiang Yang, 2018. "Sparse Bayesian Variable Selection with Correlation Prior for Forecasting Macroeconomic Variable using Highly Correlated Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 323-338, February.
    78. Yang Aijun & Xiang Ju & Yang Hongqiang & Lin Jinguan, 2018. "Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1123-1138, April.
    79. Marcelo A. T. Aragão, 2021. "Blurred Crystal Ball: investigating the forecasting challenges after a great exogenous shock," Working Papers Series 549, Central Bank of Brazil, Research Department.
    80. Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2019. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," EMF Research Papers 20, Economic Modelling and Forecasting Group.
    81. Annika Schnücker, 2016. "Restrictions Search for Panel VARs," Discussion Papers of DIW Berlin 1612, DIW Berlin, German Institute for Economic Research.
    82. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    83. Ping Wu & Gary Koop, 2022. "Fast, Order-Invariant Bayesian Inference in VARs using the Eigendecomposition of the Error Covariance Matrix," Working Papers 2310, University of Strathclyde Business School, Department of Economics.
    84. Dahem, Ahlem, 2015. "Short term Bayesian inflation forecasting for Tunisia," MPRA Paper 66702, University Library of Munich, Germany.
    85. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    86. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    87. Konstantakis, Konstantinos N. & Soklis, George & Michaelides, Panayotis G., 2017. "Tourism expenditures and crisis transmission: A general equilibrium GVAR analysis with network theory," Annals of Tourism Research, Elsevier, vol. 66(C), pages 74-94.
    88. Till Weigt & Bernd Wilfling, 2021. "An approach to increasing forecast‐combination accuracy through VAR error modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 686-699, July.
    89. Julius Stakenas, 2018. "Slicing up inflation: analysis and forecasting of Lithuanian inflation components," Bank of Lithuania Working Paper Series 56, Bank of Lithuania.
    90. Balcilar, Mehmet & Gupta, Rangan & Kotzé, Kevin, 2015. "Forecasting macroeconomic data for an emerging market with a nonlinear DSGE model," Economic Modelling, Elsevier, vol. 44(C), pages 215-228.
    91. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.
    92. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    93. Albis, Manuel Leonard F. & Mapa, Dennis S., 2014. "Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models," MPRA Paper 55902, University Library of Munich, Germany.
    94. Huang, Y-F., 2012. "Forecasting Chinese inflation and output: A Bayesian vector autoregressive approach," MPRA Paper 41933, University Library of Munich, Germany.
    95. Prüser, Jan, 2017. "Forecasting US inflation using Markov dimension switching," Ruhr Economic Papers 710, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    96. Roman Matkovskyy, 2019. "Extremal Economic (Inter)Dependence Studies: A Case of the Eastern European Countries," Post-Print hal-02332090, HAL.
    97. Mirriam Chitalu Chama-Chiliba & Rangan Gupta & Nonophile Nkambule & Naomi Tlotlego, 2011. "Forecasting Key Macroeconomic Variables of the South African Economy Using Bayesian Variable Selection," Working Papers 201132, University of Pretoria, Department of Economics.
    98. Ahlem DAHEM, 2016. "Short-Term Bayesian Inflation Forecasting For Tunisia: Some Empirical Evidence," EcoForum, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration - Economy, Business Administration and Tourism Department., vol. 5(1), pages 1-47, January.
    99. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    100. Schnücker, Annika, 2016. "Restrictions Search for Panel VARs," VfS Annual Conference 2016 (Augsburg): Demographic Change 145566, Verein für Socialpolitik / German Economic Association.

  35. Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen Rombouts, 2011. "A Comparison of Forecasting Procedures For Macroeconomic Series: The Contribution of Structural Break Models," CIRANO Working Papers 2011s-13, CIRANO.

    Cited by:

    1. Nonejad Nima, 2015. "Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 561-584, December.
    2. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    3. Pierre L. Siklos, 2020. "Looking into the Rear-View Mirror: Lessons from Japan for the Eurozone and the U.S?," IMES Discussion Paper Series 20-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
    4. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2020. "Point and Density Forecasting of Macroeconomic and Financial Uncertainties of the United States," Working Papers 202058, University of Pretoria, Department of Economics.
    5. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
    6. Todd E. Clark & Taeyoung Doh, 2011. "A Bayesian evaluation of alternative models of trend inflation," Research Working Paper RWP 11-16, Federal Reserve Bank of Kansas City.
    7. Wensheng Kang & Jing Wang, 2018. "Oil shocks, policy uncertainty and earnings surprises," Review of Quantitative Finance and Accounting, Springer, vol. 51(2), pages 375-388, August.
    8. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    9. 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.
    10. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    11. Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2015. "Forecasting with VAR Models: Fat Tails and Stochastic Volatility," CReMFi Discussion Papers 2, CReMFi, School of Economics and Finance, QMUL.
    12. Gary Koop & Dimitris Korompilis, 2009. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," Working Papers 0917, University of Strathclyde Business School, Department of Economics.
    13. Hossein Hassani & Zara Ghodsi & Rangan Gupta & Mawuli K. Segnon, 2014. "Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis," Working Papers 201482, University of Pretoria, Department of Economics.
    14. Ardia, David & Dufays, Arnaud & Ordás Criado, Carlos, 2023. "Linking Frequentist and Bayesian Change-Point Methods," MPRA Paper 119486, University Library of Munich, Germany.
    15. Luis Uzeda, 2016. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," ANU Working Papers in Economics and Econometrics 2016-632, Australian National University, College of Business and Economics, School of Economics.
    16. Koop, G & Korobilis, D, 2018. "Forecasting with High-Dimensional Panel VARs," Essex Finance Centre Working Papers 21329, University of Essex, Essex Business School.
    17. McKnight, Stephen & Mihailov, Alexander & Rumler, Fabio, 2020. "Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend," Economic Modelling, Elsevier, vol. 87(C), pages 383-393.
    18. Jiang, Feiyu & Zhao, Zifeng & Shao, Xiaofeng, 2023. "Time series analysis of COVID-19 infection curve: A change-point perspective," Journal of Econometrics, Elsevier, vol. 232(1), pages 1-17.
    19. Arnaud Dufays & Jeroen V. K. Rombouts, 2019. "Sparse Change-point HAR Models for Realized Variance," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 857-880, September.
    20. Markku Lanne & Jani Luoto, 2017. "A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 969-995, August.
    21. Hou, Chenghan, 2017. "Infinite hidden markov switching VARs with application to macroeconomic forecast," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1025-1043.
    22. Czinkota, Thomas, 2012. "Das Halteproblem bei Strukturbrüchen in Finanzmarktzeitreihen [The Halting Problem applied to Structural Breaks in Financial Time Series]," MPRA Paper 37072, University Library of Munich, Germany.
    23. Wensheng Kang & Ronald A. Ratti & Kyung Hwan Yoon, 2015. "Time-varying effect of oil market shocks on the stock market," CAMA Working Papers 2015-35, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    24. Kirsten Thompson & Renee Van Eyden & Rangan Gupta, 2015. "Identifying an index of financial conditions for South Africa," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(2), pages 256-274, June.
    25. Timmermann, Allan & Pettenuzzo, Davide, 2016. "Forecasting Macroeconomic Variables under Model Instability," CEPR Discussion Papers 11355, C.E.P.R. Discussion Papers.
    26. Kascha, Christian & Trenkler, Carsten, 2015. "Forecasting VARs, model selection, and shrinkage," Working Papers 15-07, University of Mannheim, Department of Economics.
    27. 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.
    28. Mark Fisher & Mark J. Jensen, 2018. "Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors," Working Paper series 18-12, Rimini Centre for Economic Analysis.
    29. Nonejad, Nima, 2020. "Crude oil price changes and the United Kingdom real gross domestic product growth rate: An out-of-sample investigation," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    30. Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2023. "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms," Papers 2303.01887, arXiv.org.
    31. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
    32. Dimitrios P. Louzis, 2016. "Macroeconomic forecasting and structural changes in steady states," Working Papers 204, Bank of Greece.
    33. Bauwens, Luc & De Backer, Bruno & Dufays, Arnaud, 2014. "A Bayesian method of change-point estimation with recurrent regimes: Application to GARCH models," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 207-229.
    34. Korobilis, D, 2017. "Forecasting with many predictors using message passing algorithms," Essex Finance Centre Working Papers 19565, University of Essex, Essex Business School.
    35. C. Y. Tan & Y. B. Koh & K. H. Ng & K. H. Ng, 2019. "Structural Change Analysis of Active Cryptocurrency Market," Papers 1909.10679, arXiv.org.
    36. Gary Koop & Dimitris Korobilis, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," Working Paper series 18-31, Rimini Centre for Economic Analysis.
    37. Markku Lanne & Jani Luoto, 2014. "Noncausal Bayesian Vector Autoregression," CREATES Research Papers 2014-07, Department of Economics and Business Economics, Aarhus University.
    38. Kirsten Thompson & Reneé van Eyden & Rangan Gupta, 2015. "Testing the Out-of-Sample Forecasting Ability of a Financial Conditions Index for South Africa," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(3), pages 486-501, May.
    39. Dimitris Korobilis, 2019. "High-dimensional macroeconomic forecasting using message passing algorithms," Working Papers 2019_07, Business School - Economics, University of Glasgow.
    40. Ewing, Bradley T. & Kang, Wensheng & Ratti, Ronald A., 2018. "The dynamic effects of oil supply shocks on the US stock market returns of upstream oil and gas companies," Energy Economics, Elsevier, vol. 72(C), pages 505-516.
    41. Nonejad, Nima, 2017. "Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 131-154.
    42. Eo Yunjong, 2016. "Structural changes in inflation dynamics: multiple breaks at different dates for different parameters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 211-231, June.
    43. Arnaud Dufays & Zhuo Li & Jeroen V.K. Rombouts & Yong Song, 2021. "Sparse change‐point VAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 703-727, September.
    44. Elena Afanasyeva, 2020. "Can Forecast Errors Predict Financial Crises? Exploring the Properties of a New Multivariate Credit Gap," Finance and Economics Discussion Series 2020-045, Board of Governors of the Federal Reserve System (U.S.).
    45. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2022. "Modelling the COVID‐19 infection trajectory: A piecewise linear quantile trend model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1589-1607, November.
    46. Franz Ruch & Mehmet Balcilar & Mampho P. Modise & Rangan Gupta, 2015. "Forecasting Core Inflation: The Case of South Africa," Working Papers 201543, University of Pretoria, Department of Economics.
    47. Eraslan, Sercan & Schröder, Maximilian, 2019. "Nowcasting GDP with a large factor model space," Discussion Papers 41/2019, Deutsche Bundesbank.
    48. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    49. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
    50. Augustyniak, Maciej & Dufays, Arnaud, 2018. "Modeling macroeconomic series with regime-switching models characterized by a high-dimensional state space," Economics Letters, Elsevier, vol. 170(C), pages 122-126.
    51. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2021. "Point and density forecasting of macroeconomic and financial uncertainties of the USA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 700-707, July.

  36. Koop, Gary & Korobilis, Dimitris, 2011. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," SIRE Discussion Papers 2011-39, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Mehmet Balcilar & Rangan Gupta & Clement Kyei & Mark E. Wohar, 2016. "Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test," Open Economies Review, Springer, vol. 27(2), pages 229-250, April.
    2. Konstantin Styrin, 2018. "Forecasting inflation in Russia by Dynamic Model Averaging," Bank of Russia Working Paper Series wps39, Bank of Russia.
    3. Goodness C. Aye & Rangan Gupta & Shawkat Hammoudeh & Won Joong Kim, 2014. "Forecasting the Price of Gold Using Dynamic Model Averaging," Working Papers 201415, University of Pretoria, Department of Economics.
    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. Joshua C.C. Chan & Rodney W. Strachan, 2020. "Bayesian state space models in macroeconometrics," CAMA Working Papers 2020-90, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Omokolade Akinsomi & Goodness C. Aye & Vassilios Babalos & Fotini Economou & Rangan Gupta, 2016. "Real estate returns predictability revisited: novel evidence from the US REITs market," Empirical Economics, Springer, vol. 51(3), pages 1165-1190, November.
    7. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    8. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2013. "Forecasting the US Real Private Residential Fixed Investment Using Large Number of Predictors," Working Papers 201348, University of Pretoria, Department of Economics.
    9. Gupta, Rangan & Hammoudeh, Shawkat & Kim, Won Joong & Simo-Kengne, Beatrice D., 2014. "Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals, financial stress and economic uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 170-189.
    10. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    11. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    12. Afees A. Salisu & Kazeem Isah, 2017. "Predicting US CPI-Inflation in the presence of asymmetries, persistence, endogeneity, and conditional heteroscedasticity," Working Papers 026, Centre for Econometric and Allied Research, University of Ibadan.
    13. Martin Hodula & Simona Malovana & Jan Frait, 2019. "Introducing a New Index of Households' Macroeconomic Conditions," Working Papers 2019/10, Czech National Bank.
    14. Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
    15. 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.
    16. Mihaela Simionescu (Bratu), 2014. "The Performance of Predictions Based on the Dobrescu Macromodel for the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 179-195, October.
    17. Behnamian, Mehdi & Shojaee, Abdul Nasser & Haji, Gholamali, 2021. "Investigating the Effective Factors in the Growth of Private Sector Investment in Iran," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 7(4), pages 84-57, February.
    18. MeiChi Huang, 2019. "A Nationwide or Localized Housing Crisis? Evidence from Structural Instability in US Housing Price and Volume Cycles," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1547-1563, April.
    19. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/02, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    20. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
    21. Nombulelo Gumata & Alain Kabundi & Eliphas Ndou, 2013. "Important channels of transmission of monetary policy shock in South Africa," Working Papers 6021, South African Reserve Bank.
    22. Naser, Hanan & Alaali, Fatema, 2015. "Can Oil Prices Help Predict US Stock Market Returns: An Evidence Using a DMA Approach," MPRA Paper 65295, University Library of Munich, Germany, revised 25 Jun 2015.
    23. Fasanya, Ismail & Adekoya, Oluwasegun & Oyewole, Oluwatomisin & Adegboyega, Soliu, 2022. "Investor sentiment and energy futures predictability: Evidence from Feasible Quasi Generalized Least Squares," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    24. Pallara, Kevin, 2016. "The dynamic effects of government spending: a FAVAR approach," MPRA Paper 92283, University Library of Munich, Germany.
    25. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    26. Michelle, Gilmartin, 2016. "A note on the identification and transmission of energy demand and supply shocks," MPRA Paper 76186, University Library of Munich, Germany.
    27. Naik, Prasad A., 2015. "Marketing Dynamics: A Primer on Estimation and Control," Foundations and Trends(R) in Marketing, now publishers, vol. 9(3), pages 175-266, December.
    28. Wang, Tiantian & Qu, Wan & Zhang, Dayong & Ji, Qiang & Wu, Fei, 2022. "Time-varying determinants of China's liquefied natural gas import price: A dynamic model averaging approach," Energy, Elsevier, vol. 259(C).
    29. Eraslan, Sercan & Schröder, Maximilian, 2023. "Nowcasting GDP with a pool of factor models and a fast estimation algorithm," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1460-1476.
    30. Nicholas Apergis & Ghassen El Montasser & Emmanuel Owusu-Sekyere & Ahdi N. Ajmi & Rangan Gupta, 2014. "Dutch Disease Effect of Oil Rents on Agriculture Value Added in MENA Countries," Working Papers 201408, University of Pretoria, Department of Economics.
    31. Zhongxin Ni & Xing Lu & Wenjun Xue, 2021. "Does the belt and road initiative resolve the steel overcapacity in China? Evidence from a dynamic model averaging approach," Empirical Economics, Springer, vol. 61(1), pages 279-307, July.
    32. Ouysse, Rachida, 2016. "Bayesian model averaging and principal component regression forecasts in a data rich environment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 763-787.
    33. Afees A. Salisu & Kazeem Isah, 2017. "Predicting US Inflation: Evidence from a New Approach," Working Papers 039, Centre for Econometric and Allied Research, University of Ibadan.
    34. Dong, Xiyong & Yoon, Seong-Min, 2019. "What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach," Economic Modelling, Elsevier, vol. 77(C), pages 204-215.
    35. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    36. Hyeyoen Kim & Doojin Ryu, 2013. "Forecasting Exchange Rate from Combination Taylor Rule Fundamental," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S4), pages 81-92, September.
    37. Jan Prüser, 2019. "Adaptive learning from model space," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(1), pages 29-38, January.
    38. Wei, Yu & Cao, Yang, 2017. "Forecasting house prices using dynamic model averaging approach: Evidence from China," Economic Modelling, Elsevier, vol. 61(C), pages 147-155.
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    40. Mohsen Khezri & Seyed Ehsan Hosseinidoust & Mohammad Kazem Naziri, 2019. "Investigating the Temporary and Permanent Influential Variables on Iran Inflation Using TVP-DMA Models," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 23(1), pages 209-234, Winter.
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    1. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
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    3. Christian Hotz‐Behofsits & Florian Huber & Thomas Otto Zörner, 2018. "Predicting crypto‐currencies using sparse non‐Gaussian state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 627-640, September.
    4. Bitto, Angela & Frühwirth-Schnatter, Sylvia, 2019. "Achieving shrinkage in a time-varying parameter model framework," Journal of Econometrics, Elsevier, vol. 210(1), pages 75-97.
    5. Silvia Miranda Agrippino & Giovanni Ricco, 2018. "Bayesian vector autoregressions," SciencePo Working papers Main hal-03458277, HAL.
    6. Dufays, A. & Rombouts, V., 2015. "Sparse Change-Point Time Series Models," LIDAM Discussion Papers CORE 2015032, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    8. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    9. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    10. Gregor Kastner, 2016. "Sparse Bayesian time-varying covariance estimation in many dimensions," Papers 1608.08468, arXiv.org, revised Nov 2017.
    11. Joshua C.C. Chan & Rodney W. Strachan, 2020. "Bayesian state space models in macroeconometrics," CAMA Working Papers 2020-90, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Annalisa Cadonna & Sylvia Frühwirth-Schnatter & Peter Knaus, 2020. "Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models," Econometrics, MDPI, vol. 8(2), pages 1-36, May.
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    39. Michelle, Gilmartin, 2016. "A note on the identification and transmission of energy demand and supply shocks," MPRA Paper 76186, University Library of Munich, Germany.
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    45. Damian Stelmasiak & Grzegorz Szafrański, 2016. "Forecasting the Polish Inflation Using Bayesian VAR Models with Seasonality," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 21-42, March.
    46. Dimitris Korobilis, 2019. "High-dimensional macroeconomic forecasting using message passing algorithms," Working Papers 2019_07, Business School - Economics, University of Glasgow.
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    51. Yang Aijun & Xiang Ju & Yang Hongqiang & Lin Jinguan, 2018. "Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1123-1138, April.
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    1. Pfeifer, Lukáš & Hodula, Martin, 2021. "A profit-to-provisioning approach to setting the countercyclical capital buffer," Economic Systems, Elsevier, vol. 45(1).
    2. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Exchange Rates under Model and Parameter Uncertainty," CQE Working Papers 3214, Center for Quantitative Economics (CQE), University of Muenster.
    3. Jaromir Baxa & Miroslav Plasil & Borek Vasicek, 2013. "Inflation and the Steeplechase Between Economic Activity Variables," Working Papers 2013/15, Czech National Bank.
    4. KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," LIDAM Discussion Papers CORE 2011021, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. 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.
    6. Hassani, Hossein & Silva, Emmanuel Sirimal, 2018. "Forecasting UK consumer price inflation using inflation forecasts," Research in Economics, Elsevier, vol. 72(3), pages 367-378.
    7. Mehmet Balcilar & Rangan Gupta & Clement Kyei & Mark E. Wohar, 2016. "Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test," Open Economies Review, Springer, vol. 27(2), pages 229-250, April.
    8. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    9. Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios & Gupta, Rangan, 2019. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," DUTH Research Papers in Economics 3-2016, Democritus University of Thrace, Department of Economics.
    10. Le Ha Thu & Roberto Leon-Gonzalez, 2021. "Forecasting Macroeconomic Variables in Emerging Economies: An Application to Vietnam," GRIPS Discussion Papers 21-03, National Graduate Institute for Policy Studies.
    11. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
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    15. Doojav Gan-Ochir & Luvsannyam Davaajargal, 2023. "Forecasting Inflation in Mongolia: A Dynamic Model Averaging Approach," Journal of Time Series Econometrics, De Gruyter, vol. 15(1), pages 27-48, January.
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    22. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It's all about volatility of volatility: evidence from a two-factor stochastic volatility model," Studies in Economics 1404, School of Economics, University of Kent.
    23. Goodness C. Aye & Rangan Gupta & Shawkat Hammoudeh & Won Joong Kim, 2014. "Forecasting the Price of Gold Using Dynamic Model Averaging," Working Papers 201415, University of Pretoria, Department of Economics.
    24. Alain Kabundi & Asithandile Mbelu, 2021. "Estimating a time-varying financial conditions index for South Africa," Empirical Economics, Springer, vol. 60(4), pages 1817-1844, April.
    25. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    26. 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.
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    31. Marcos Álvarez-Díaz & Rangan Gupta, 2015. "Forecasting the US CPI: Does Nonlinearity Matter?," Working Papers 201512, University of Pretoria, Department of Economics.
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    33. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
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    37. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    38. Omokolade Akinsomi & Goodness C. Aye & Vassilios Babalos & Fotini Economou & Rangan Gupta, 2016. "Real estate returns predictability revisited: novel evidence from the US REITs market," Empirical Economics, Springer, vol. 51(3), pages 1165-1190, November.
    39. Gary Koop & Dimitris Korompilis, 2009. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," Working Papers 0917, University of Strathclyde Business School, Department of Economics.
    40. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    41. Tortorice, Daniel L., 2018. "Equity return predictability, time varying volatility and learning about the permanence of shocks," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 315-343.
    42. Jeffrey S. Racine & Qi Li & Li Zheng, 2018. "Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions," Department of Economics Working Papers 2018-10, McMaster University.
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    193. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2021. "Variants of consumption‐wealth ratios and predictability of U.S. government bond risk premia," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 661-674, June.
    194. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
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    204. Danglun Luo & Qianwei Ying, 2014. "Political Connections and Bank Lines of Credit," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(03), pages 5-21, May.
    205. Dong, Xiyong & Yoon, Seong-Min, 2023. "Effect of weather and environmental attentions on financial system risks: Evidence from Chinese high- and low-carbon assets," Energy Economics, Elsevier, vol. 121(C).
    206. Prüser, Jan, 2017. "Forecasting US inflation using Markov dimension switching," Ruhr Economic Papers 710, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    207. Bakerman, Jordan & Pazdernik, Karl & Korkmaz, Gizem & Wilson, Alyson G., 2022. "Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest," International Journal of Forecasting, Elsevier, vol. 38(2), pages 648-661.
    208. Tomás Marinozzi, 2023. "Forecasting Inflation in Argentina: A Probabilistic Approach," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(81), pages 81-110, May.
    209. Camilla Muglia & Luca Santabarbara & Stefano Grassi, 2019. "Is Bitcoin a Relevant Predictor of Standard & Poor’s 500?," JRFM, MDPI, vol. 12(2), pages 1-10, May.
    210. Dorra Zouari & Achraf Ghorbel & Sonia Ghorbel-Zouari & Younes Boujelbène, 2014. "Volatility spillovers and dynamic correlation between liquidity risk factors in Tunisian banks," International Journal of Managerial and Financial Accounting, Inderscience Enterprises Ltd, vol. 6(1), pages 1-26.
    211. Simona Malovaná & Martin Hodula & Jan Frait, 2021. "What Does Really Drive Consumer Confidence?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(3), pages 885-913, June.
    212. Lin, Jilei & Eck, Daniel J., 2021. "Minimizing post-shock forecasting error through aggregation of outside information," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1710-1727.
    213. Gadea-Rivas, María Dolores & Gómez-Loscos, Ana & Leiva-Leon, Danilo, 2019. "Increasing linkages among European regions. The role of sectoral composition," Economic Modelling, Elsevier, vol. 80(C), pages 222-243.
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    215. Konstantin Styrin, 2019. "Forecasting Inflation in Russia Using Dynamic Model Averaging," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 3-18, March.
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    217. Rangan Gupta & Shawkat Hammoudeh & Beatrice D. Simo-Kengne & Soodabeh Sarafrazi, 2013. "Can the Sharia-Based Islamic Stock Market Returns be Forecasted Using Large Number of Predictors and Models?," Working Papers 201381, University of Pretoria, Department of Economics.
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    219. Byrne, Joseph & Fu, Rong, 2016. "Stock Return Prediction with Fully Flexible Models and Coefficients," MPRA Paper 75366, University Library of Munich, Germany.
    220. Luis Gruber & Gregor Kastner, 2022. "Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!," Papers 2206.04902, arXiv.org, revised Jul 2023.
    221. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
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  39. Korobilis, Dimitris & Gilmartin, Michelle, 2010. "On regional unemployment: an empirical examination of the determinants of geographical differentials in the UK," MPRA Paper 28542, University Library of Munich, Germany.

    Cited by:

    1. Piotr Ciżkowicz & Michał Kowalczuk & Andrzej Rzońca, 2016. "Heterogeneous determinants of local unemployment in Poland," Post-Communist Economies, Taylor & Francis Journals, vol. 28(4), pages 487-519, October.
    2. Qingyu, Zhu, 2010. "Regional unemployment and house price determination," MPRA Paper 41785, University Library of Munich, Germany.
    3. Donald Houston, 2020. "Local resistance to rising unemployment in the context of the COVID‐19 mitigation policies across Great Britain," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1189-1209, December.
    4. George Grekousis, 2018. "Further Widening or Bridging the Gap? A Cross-Regional Study of Unemployment across the EU Amid Economic Crisis," Sustainability, MDPI, vol. 10(6), pages 1-18, May.
    5. George Grekousis & Stelios Gialis, 2019. "More Flexible Yet Less Developed? Spatio-Temporal Analysis of Labor Flexibilization and Gross Domestic Product in Crisis-Hit European Union Regions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(2), pages 505-524, June.
    6. César Augusto MERCHÁN HERNÁNDEZ, 2014. "Desempleo y ocupación en las ciudades colombianas. Un ejercicio con datos panel," Archivos de Economía 11212, Departamento Nacional de Planeación.
    7. Kevin Ralston & Dawn Everington & Zhiqiang Feng & Chris Dibben, 2022. "Economic Inactivity, Not in Employment, Education or Training (NEET) and Scarring: The Importance of NEET as a Marker of Long-Term Disadvantage," Work, Employment & Society, British Sociological Association, vol. 36(1), pages 59-79, February.

  40. Korobilis, Dimitris & Gilmartin, Michelle, 2010. "The dynamic effects of U.S. monetary policy on state unemployment," MPRA Paper 27596, University Library of Munich, Germany.

    Cited by:

    1. Barkhordari, Sajjad & Forughi Far, Mohsen, 2020. "The Dynamic Regional Effects of Monetary Policy on Employment in Iran (TVP-FAVAR Approach)," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 6(4), pages 109-136, February.
    2. Irfan akbar Kazi & Hakimzadi Wagan & Farhan Akbar, 2011. "The changing international transmission of us monetary policy shocks: is there evidence of contagion effect on oecd countries," Economics Bulletin, AccessEcon, vol. 31(4), pages 1-49.
    3. Irfan Akbar Kazi & Hakimzadi Wagan & Farhan Akbar, 2012. "The changing international transmission of US monetary policy shocks: is there evidence of contagion effect on OECD countries," Working Papers hal-04141067, HAL.
    4. Qingyu, Zhu, 2010. "Regional unemployment and house price determination," MPRA Paper 41785, University Library of Munich, Germany.

  41. Korobilis, Dimitris, 2009. "Assessing the transmission of monetary policy using dynamic factor models," MPRA Paper 27593, University Library of Munich, Germany, revised Nov 2010.

    Cited by:

    1. Tsionas, Efthymios G. & Konstantakis, Konstantinos N. & Michaelides, Panayotis G., 2016. "Bayesian GVAR with k-endogenous dominants & input–output weights: Financial and trade channels in crisis transmission for BRICs," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 42(C), pages 1-26.
    2. Urga, Giovanni & Wang, Fa, 2022. "Estimation and inference for high dimensional factor model with regime switching," MPRA Paper 113172, University Library of Munich, Germany.
    3. Brandon J. Bates & Mikkel Plagborg-Møller & James H. Stock & Mark W. Watson, "undated". "Consistent factor estimation in dynamic factor models with structural instability," Working Paper 84631, Harvard University OpenScholar.
    4. Davide Debortoli & Ricardo Nunes, 2014. "Monetary Regime Switches and Central Bank Preferences," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(8), pages 1591-1626, December.
    5. Gary Koop & Dimitris Korobilis, 2011. "Forecasting Inflation Using Dynamic Model Averaging," Working Papers 1119, University of Strathclyde Business School, Department of Economics.
    6. Luisa Corrado & Stefano Grassi & Enrico Minnella, 2021. "The Transmission Mechanism of Quantitative Easing: A Markov-Switching FAVAR Approach," CEIS Research Paper 520, Tor Vergata University, CEIS, revised 21 Oct 2021.
    7. Dimitris Korobilis & Maximilian Schröder, 2023. "Monitoring multicountry macroeconomic risk," Working Papers No 06/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    8. Martin Feldkircher & Florian Huber, 2018. "Unconventional U.S. Monetary Policy: New Tools, Same Channels?," JRFM, MDPI, vol. 11(4), pages 1-31, October.
    9. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    10. 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.
    11. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    12. Florian Huber & Maria Teresa Punzi, 2016. "International Housing Markets, Unconventional Monetary Policy and the Zero Lower Bound," Department of Economics Working Papers wuwp216, Vienna University of Economics and Business, Department of Economics.
    13. Andrew S. Duncan & Alain Kabundi, 2014. "Global Financial Crises and Time-Varying Volatility Comovement in World Equity Markets," South African Journal of Economics, Economic Society of South Africa, vol. 82(4), pages 531-550, December.
    14. Huber, Florian & Fischer, Manfred M., 2015. "A Markov switching factor-augmented VAR model for analyzing US business cycles and monetary policy," Department of Economics Working Paper Series 201, WU Vienna University of Economics and Business.
    15. Prüser, Jan & Schlösser, Alexander, 2017. "The effects of economic policy uncertainty on European economies: Evidence from a TVP-FAVAR," Ruhr Economic Papers 708, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    16. Joshua C.C. Chan & Rodney W. Strachan, 2020. "Bayesian state space models in macroeconometrics," CAMA Working Papers 2020-90, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Dimitris, Korobilis, 2013. "Forecasting with Factor Models: A Bayesian Model Averaging Perspective," MPRA Paper 52724, University Library of Munich, Germany.
    18. Beatrice D. Simo-Kengne & Stephen M. Miller & Rangan Gupta, 2014. "Evolution of the Monetary Transmission Mechanism in the US: The Role of Asset Returns," Working Papers 1405, University of Nevada, Las Vegas , Department of Economics.
    19. Zakaria Moussa, 2016. "How big is the comeback? Japanese exchange rate pass-through assessed by time-varying FAVAR," Post-Print hal-03714934, HAL.
    20. Dhital, Saroj & Jiang, Senyuan & Reese, Jillian, 2023. "Effects of monetary and government spending policy on economic inequality," Journal of Macroeconomics, Elsevier, vol. 77(C).
    21. C. Glocker & G. Sestieri & P. Towbin, 2017. "Time-varying fiscal spending multipliers in the UK," Working papers 643, Banque de France.
    22. Hartigan, Luke & Morley, James, 2019. "A Factor Model Analysis of the Australian Economy and the Effects of Inflation Targeting," Working Papers 2019-10, University of Sydney, School of Economics, revised Nov 2019.
    23. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    24. Yanhong Feng & Dilong Xu & Pierre Failler & Tinghui Li, 2020. "Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation," Sustainability, MDPI, vol. 12(16), pages 1-24, August.
    25. Magnus Reif, 2021. "Time-Varying Dynamics of the German Business Cycle: A Comprehensive Investigation," CESifo Working Paper Series 9271, CESifo.
    26. Eickmeier, Sandra & Lemke, Wolfgang & Marcellino, Massimiliano, 2011. "Classical time-varying FAVAR models - estimation, forecasting and structural analysis," Discussion Paper Series 1: Economic Studies 2011,04, Deutsche Bundesbank.
    27. Hardik A. Marfatia & Christophe Andre & Rangan Gupta, 2020. "Predicting Housing Market Sentiment: The Role of Financial, Macroeconomic and Real Estate Uncertainties," Working Papers 202061, University of Pretoria, Department of Economics.
    28. Hilde C. Bjørnland & Julia Zhulanova, 2019. "The shale oil boom and the U.S. economy: Spillovers and time-varying effects," Working Paper 2019/14, Norges Bank.
    29. Raputsoane, Leroi, 2018. "Monetary policy reaction function pre and post the global financial crisis," MPRA Paper 84866, University Library of Munich, Germany.
    30. Huber, Florian, 2016. "Density forecasting using Bayesian global vector autoregressions with stochastic volatility," International Journal of Forecasting, Elsevier, vol. 32(3), pages 818-837.
    31. Beatrice D. Simo-Kengne & Stephen M. Miller & Rangan Gupta, 2013. "Evolution of Monetary Policy in the US: The Role of Asset Prices," Working papers 2013-20, University of Connecticut, Department of Economics, revised Dec 2013.
    32. Jouchi Nakajima, 2011. "Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications," IMES Discussion Paper Series 11-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
    33. Koop, Gary & Korobilis, Dimitris, 2013. "A New Index of Financial Conditions," MPRA Paper 45463, University Library of Munich, Germany.
    34. Gary Koop & Dimitris Korobilis, 2009. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Working Paper series 47_09, Rimini Centre for Economic Analysis.
    35. Robert A. Hill & Paulo M. M. Rodrigues, 2022. "Forgetting approaches to improve forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1356-1371, November.
    36. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    37. Borzykh, Olga, 2016. "Bank lending channel in Russia: A TVP-FAVAR approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 96-117.
    38. Pacicco, Fausto & Serati, Massimiliano & Venegoni, Andrea, 2022. "The Euro Area credit crunch conundrum: Was it demand or supply driven?," Economic Modelling, Elsevier, vol. 106(C).
    39. Serati, Massimiliano & Venegoni, Andrea, 2019. "The cross-country impact of ECB policies: Asymmetries in – Asymmetries out?," Journal of International Money and Finance, Elsevier, vol. 90(C), pages 118-141.
    40. Samuel Addo, 2018. "Policy regime changes and central bank prefernces," Working Papers 752, Economic Research Southern Africa.
    41. Wensheng Kang & Ronald A. Ratti & Kyung Hwan Yoon, 2015. "Time-varying effect of oil market shocks on the stock market," CAMA Working Papers 2015-35, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    42. MOLTENI, Francesco, PAPPA, Evi, 2017. "The Combination of Monetary and Fiscal Policy Shocks: A TVP-FAVAR Approach," Economics Working Papers MWP 2017/13, European University Institute.
    43. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    44. Eickmeier, Sandra & Lemke, Wolfgang & Marcellino, Massimiliano, 2011. "The changing international transmission of financial shocks: evidence from a classical time-varying FAVAR," Discussion Paper Series 1: Economic Studies 2011,05, Deutsche Bundesbank.
    45. Irfan akbar Kazi & Hakimzadi Wagan & Farhan Akbar, 2011. "The changing international transmission of us monetary policy shocks: is there evidence of contagion effect on oecd countries," Economics Bulletin, AccessEcon, vol. 31(4), pages 1-49.
    46. Njindan Iyke, Bernard, 2016. "Are Monetary Policy Disturbances Important in Ghana? Some Evidence from Agnostic Identification," MPRA Paper 70205, University Library of Munich, Germany.
    47. Omoshoro-Jones, Oyeyinka Sunday & Bonga-Bonga, Lumengo, 2020. "Intra-regional spillovers from Nigeria and South Africa to the rest of Africa: New evidence from a FAVAR model," MPRA Paper 99514, University Library of Munich, Germany.
    48. Michaelides, Panayotis G. & Tsionas, Efthymios G. & Konstantakis, Konstantinos N., 2018. "Debt Crisis in Europe (2001-2015): A Network General Equilibrium GVAR approach," MPRA Paper 89998, University Library of Munich, Germany.
    49. Gil-Alana, Luis A. & Gupta, Rangan & Olubusoye, Olusanya E. & Yaya, OlaOluwa S., 2016. "Time series analysis of persistence in crude oil price volatility across bull and bear regimes," Energy, Elsevier, vol. 109(C), pages 29-37.
    50. Glocker, Christian & Sestieri, Giulia & Towbin, Pascal, 2019. "Time-varying government spending multipliers in the UK," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 180-197.
    51. Marcellino, Massimiliano & Eickmeier, Sandra & Prieto, Esteban, 2013. "Time Variation in Macro-Financial Linkages," CEPR Discussion Papers 9436, C.E.P.R. Discussion Papers.
    52. Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Tsionas, Efthymios G. & Minou, Chrysanthi, 2015. "System estimation of GVAR with two dominants and network theory: Evidence for BRICs," Economic Modelling, Elsevier, vol. 51(C), pages 604-616.
    53. Urga, Giovanni & Wang, Fa, 2022. "Estimation and Inference for High Dimensional Factor Model with Regime Switching," MPRA Paper 117012, University Library of Munich, Germany, revised 10 Apr 2023.
    54. Tavakolian , Hossein & Babaee , Majid & Shakeri , Abbas, 2018. "How Fluctuations in Macroeconomic Indicators Affect Inflation in Iran," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 13(3), pages 267-289, July.
    55. Michelle, Gilmartin, 2016. "A note on the identification and transmission of energy demand and supply shocks," MPRA Paper 76186, University Library of Munich, Germany.
    56. IIBOSHI, Hirokuni & IWATA, Yasuharu, 2023. "The Nexus between Public Debt and the Government Spending Multiplier: Fiscal Adjustments Matter," MPRA Paper 116310, University Library of Munich, Germany.
    57. Onur AKKAYA & Mustafa ÖZER & Özcan ÖZKAN, 2019. "The Central Bank of Turkey’s response to the global currency markets," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 10, pages 249-262, December.
    58. Masud Alam, 2021. "Output, Employment, and Price Effects of U.S. Narrative Tax Changes: A Factor-Augmented Vector Autoregression Approach," Papers 2106.10844, arXiv.org.
    59. Zulfiqar Ali Wagan & Zhang Chen & Hakimzadi Wagan, 2019. "A Factor-Augmented Vector Autoregressive Approach to Analyze the Transmission of Monetary Policy," Prague Economic Papers, Prague University of Economics and Business, vol. 2019(6), pages 709-728.
    60. Anastasios Evgenidis & Dionisis Philippas & Costas Siriopoulos, 2019. "Heterogeneous effects in the international transmission of the US monetary policy: a factor-augmented VAR perspective," Empirical Economics, Springer, vol. 56(5), pages 1549-1579, May.
    61. Hacioglu, Sinem & Tuzcuoglu, Kerem, 2016. "Interpreting the latent dynamic factors by threshold FAVAR model," Bank of England working papers 622, Bank of England.
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    70. Miguel Belmonte & Gary Koop, 2013. "Model Switching and Model Averaging in Time-Varying Parameter Regression Models," Working Papers 1302, University of Strathclyde Business School, Department of Economics.
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    6. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
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    61. Hoang, Nam & Grieb, Terrance, 2018. "Hedging Positions, Basis, and Futures Risk Premium: A Disaggregated Data Analysis on US Wheat Markets," 2018 Annual Meeting, August 5-7, Washington, D.C. 273799, Agricultural and Applied Economics Association.
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    407. Michele Campolieti & Deborah Gefang & Gary Koop, 2013. "A new look at variation in employment growth in Canada," Working Papers 26145565, Lancaster University Management School, Economics Department.
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  43. Korobilis, Dimitris, 2008. "Forecasting in vector autoregressions with many predictors," MPRA Paper 21122, University Library of Munich, Germany.

    Cited by:

    1. SENBETA, Sisay Regassa, 2012. "How important are external shocks in explaining growth in Sub-Saharan Africa? Evidence from a Bayesian VAR," Working Papers 2012010, University of Antwerp, Faculty of Business and Economics.
    2. Gary Koop, 2010. "Forecasting with Medium and Large Bayesian VARs," Working Paper series 43_10, Rimini Centre for Economic Analysis.
    3. Fuentes-Albero, Cristina & Melosi, Leonardo, 2013. "Methods for computing marginal data densities from the Gibbs output," Journal of Econometrics, Elsevier, vol. 175(2), pages 132-141.
    4. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
    5. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working papers 2012-38, University of Connecticut, Department of Economics, revised Dec 2013.
    6. Dimitris, Korobilis, 2013. "Forecasting with Factor Models: A Bayesian Model Averaging Perspective," MPRA Paper 52724, University Library of Munich, Germany.
    7. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
    8. Korobilis, Dimitris, 2015. "Prior selection for panel vector autoregressions," MPRA Paper 64143, University Library of Munich, Germany.
    9. Gary Koop & Dimitris Korobilis, 2009. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Working Paper series 47_09, Rimini Centre for Economic Analysis.
    10. Louzis Dimitrios P., 2016. "Steady-state priors and Bayesian variable selection in VAR forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(5), pages 495-527, December.
    11. Maciej Stefański, 2021. "Macroeconomic Effects of Quantitative Easing Using Mid-sized Bayesian Vector Autoregressions," KAE Working Papers 2021-068, Warsaw School of Economics, Collegium of Economic Analysis.
    12. Stefański, Maciej, 2022. "Macroeconomic effects and transmission channels of quantitative easing," Economic Modelling, Elsevier, vol. 114(C).
    13. Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
    14. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    15. Chin, Kuo-Hsuan & Li, Xue, 2019. "Bayesian forecast combination in VAR-DSGE models," Journal of Macroeconomics, Elsevier, vol. 59(C), pages 278-298.
    16. Michelle, Gilmartin, 2016. "A note on the identification and transmission of energy demand and supply shocks," MPRA Paper 76186, University Library of Munich, Germany.
    17. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    18. Ouysse, Rachida, 2016. "Bayesian model averaging and principal component regression forecasts in a data rich environment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 763-787.
    19. Markus Jochmann & Gary Koop & Rodney W. Strachan, 2008. "Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks," Working Paper series 19_08, Rimini Centre for Economic Analysis.
    20. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    21. Gupta, Rangan & Steinbach, Rudi, 2013. "A DSGE-VAR model for forecasting key South African macroeconomic variables," Economic Modelling, Elsevier, vol. 33(C), pages 19-33.
    22. Annika Schnücker, 2016. "Restrictions Search for Panel VARs," Discussion Papers of DIW Berlin 1612, DIW Berlin, German Institute for Economic Research.
    23. Tomasz Woźniak, 2016. "Bayesian Vector Autoregressions," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(3), pages 365-380, September.
    24. Huang, Y-F., 2012. "Forecasting Chinese inflation and output: A Bayesian vector autoregressive approach," MPRA Paper 41933, University Library of Munich, Germany.
    25. Tomasz Wozniak, 2016. "Rare Events and Risk Perception: Evidence from Fukushima Accident," Department of Economics - Working Papers Series 2021, The University of Melbourne.
    26. Mirriam Chitalu Chama-Chiliba & Rangan Gupta & Nonophile Nkambule & Naomi Tlotlego, 2011. "Forecasting Key Macroeconomic Variables of the South African Economy Using Bayesian Variable Selection," Working Papers 201132, University of Pretoria, Department of Economics.
    27. Malefaki, Valia, 2015. "On Flexible Linear Factor Stochastic Volatility Models," MPRA Paper 62216, University Library of Munich, Germany.
    28. Alessia Paccagnini, 2017. "Forecasting with FAVAR: macroeconomic versus financial factors," NBP Working Papers 256, Narodowy Bank Polski.
    29. Schnücker, Annika, 2016. "Restrictions Search for Panel VARs," VfS Annual Conference 2016 (Augsburg): Demographic Change 145566, Verein für Socialpolitik / German Economic Association.

Articles

  1. 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.
    See citations under working paper version above.
  2. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
    See citations under working paper version above.
  3. Korobilis, Dimitris, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," European Economic Review, Elsevier, vol. 148(C).
    See citations under working paper version above.
  4. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    See citations under working paper version above.
  5. Dimitris Korobilis, 2021. "High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 493-504, March.
    See citations under working paper version above.
  6. Joscha Beckmann & Gary Koop & Dimitris Korobilis & Rainer Alexander Schüssler, 2020. "Exchange rate predictability and dynamic Bayesian learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 410-421, June.
    See citations under working paper version above.
  7. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    See citations under working paper version above.
  8. Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Adaptive hierarchical priors for high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 212(1), pages 241-271.
    See citations under working paper version above.
  9. Gary Koop & Dimitris Korobilis, 2019. "Forecasting with High‐Dimensional Panel VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 937-959, October.
    See citations under working paper version above.
  10. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2019. "Decomposing global yield curve co-movement," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 500-513.
    See citations under working paper version above.
  11. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
    See citations under working paper version above.
  12. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.

    Cited by:

    1. Georgios Bampinas & Theodore Panagiotidis & Georgios Papapanagiotou, 2022. "Oil shocks and investor attention," Working Paper series 22-13, Rimini Centre for Economic Analysis.
    2. 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.
    3. Dimitris Korobilis & Maximilian Schröder, 2023. "Monitoring multicountry macroeconomic risk," Working Papers No 06/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
    5. 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.
    6. Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
    7. Tony Chernis & Patrick J. Coe & Shaun P. Vahey, 2022. "Reassessing the dependence between economic growth and financial conditions since 1973," CAMA Working Papers 2022-30, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Xindi Wang & Zeshui Xu & Xinxin Wang & Marinko Skare, 2022. "A review of inflation from 1906 to 2022: a comprehensive analysis of inflation studies from a global perspective," Oeconomia Copernicana, Institute of Economic Research, vol. 13(3), pages 595-631, September.
    9. Ferrara, Laurent & Yapi, Joseph, 2022. "Measuring exchange rate risks during periods of uncertainty," International Economics, Elsevier, vol. 170(C), pages 202-212.
    10. Jean-Guillaume Sahuc & Matteo Mogliani & Laurent Ferrara, 2022. "High-frequency monitoring of growth at risk," Post-Print hal-03361425, HAL.
    11. López-Salido, J David & Loria, Francesca, 2019. "Inflation at Risk," CEPR Discussion Papers 14074, C.E.P.R. Discussion Papers.
    12. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
    13. Bo Zeng & Shuliang Li & Wei Meng & Dehai Zhang, 2019. "An improved gray prediction model for China’s beef consumption forecasting," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-18, September.
    14. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org.
    15. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    16. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.
    17. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.
    18. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2024. "A Bayesian approach for the determinants of bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 91(C).
    19. J. David López-Salido & Francesca Loria, 2020. "Inflation at Risk," Finance and Economics Discussion Series 2020-013, Board of Governors of the Federal Reserve System (U.S.).
    20. Sokol, Andrej & Eguren-Martin, Fernando, 2020. "Attention to the tail(s): global financial conditions and exchange rate risks," Working Paper Series 2387, European Central Bank.
    21. Chalmovianský, Jakub & Porqueddu, Mario & Sokol, Andrej, 2020. "Weigh(t)ing the basket: aggregate and component-based inflation forecasts for the euro area," Working Paper Series 2501, European Central Bank.
    22. Gupta, Rangan & Ji, Qiang & Pierdzioch, Christian & Plakandaras, Vasilios, 2023. "Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023," Finance Research Letters, Elsevier, vol. 58(PC).
    23. 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.
    24. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    25. James Mitchell & Saeed Zaman, 2023. "The Distributional Predictive Content of Measures of Inflation Expectations," Working Papers 23-31, Federal Reserve Bank of Cleveland.
    26. S. Béreau & V. Faubert & K. Schmidt, 2018. "Explaining and Forecasting Euro Area Inflation: the Role of Domestic and Global Factors," Working papers 663, Banque de France.
    27. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2020. "The role of real estate uncertainty in predicting US home sales growth: evidence from a quantiles-based Bayesian model averaging approach," Applied Economics, Taylor & Francis Journals, vol. 52(5), pages 528-536, January.
    28. Oguzhan Cepni & Rangan Gupta & Mark E. Wohar, 2019. "Variants of Consumption-Wealth Ratios and Predictability of U.S. Government Bond Risk Premia: Old is still Gold," Working Papers 201912, University of Pretoria, Department of Economics.
    29. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    30. Luo, Jiawen & Klein, Tony & Walther, Thomas & Ji, Qiang, 2021. "Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning," QBS Working Paper Series 2021/04, Queen's University Belfast, Queen's Business School.
    31. Stella W. Self & Christopher S. McMahan & Brook T. Russell, 2021. "Identifying meteorological drivers of PM2.5 levels via a Bayesian spatial quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(5), August.
    32. Milan Szabo, 2020. "Growth-at-Risk: Bayesian Approach," Working Papers 2020/3, Czech National Bank.
    33. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
    34. Stolbov, Mikhail & Shchepeleva, Maria, 2022. "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    35. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    36. Dimitris Korobilis & Maximilian Schröder, 2023. "Probabilistic Quantile Factor Analysis," Working Papers No 05/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    37. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2021. "Variants of consumption‐wealth ratios and predictability of U.S. government bond risk premia," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 661-674, June.
    38. David Kohns & Tibor Szendrei, 2020. "Horseshoe Prior Bayesian Quantile Regression," Papers 2006.07655, arXiv.org, revised Mar 2021.
    39. Marian Vavra, 2023. "Bias-Correction in Time Series Quantile Regression Models," Working and Discussion Papers WP 3/2023, Research Department, National Bank of Slovakia.
    40. Niango Ange Joseph Yapi, 2020. "Exchange rate predictive densities and currency risks: A quantile regression approach," EconomiX Working Papers 2020-16, University of Paris Nanterre, EconomiX.
    41. 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.
    42. 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.
    43. Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021. "The time-varying evolution of inflation risks," Working Paper Series 2600, European Central Bank.

  13. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.

    Cited by:

    1. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models," BAFFI CAREFIN Working Papers 19106, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    2. 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.
    3. João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4312-4329, December.
    4. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "A functional time series analysis of forward curves derived from commodity futures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 646-665.
    5. Huber, Florian & Kastner, Gregor & Feldkircher, Martin, 2016. "Should I stay or should I go? Bayesian inference in the threshold time varying parameter (TTVP) model," Department of Economics Working Paper Series 235, WU Vienna University of Economics and Business.
    6. Guidolin, Massimo & Pedio, Manuela, 2019. "Forecasting and trading monetary policy effects on the riskless yield curve with regime switching Nelson–Siegel models," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    7. Sakar Hasan Hamza & Qingna Li, 2023. "The Dynamics of US Gasoline Demand and Its Prediction: An Extended Dynamic Model Averaging Approach," Energies, MDPI, vol. 16(12), pages 1-13, June.
    8. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Florian Huber & Gregor Kastner & Martin Feldkircher, 2019. "Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 621-640, August.
    10. Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2017. "The Role of Time-Varying Rare Disaster Risks in Predicting Bond Returns and Volatility," Working Papers 201770, University of Pretoria, Department of Economics.

  14. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2016. "Exchange rate predictability in a changing world," Journal of International Money and Finance, Elsevier, vol. 62(C), pages 1-24.
    See citations under working paper version above.
  15. Korobilis, Dimitris, 2016. "Prior selection for panel vector autoregressions," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 110-120.
    See citations under working paper version above.
  16. Koop, Gary & Korobilis, Dimitris, 2016. "Model uncertainty in Panel Vector Autoregressive models," European Economic Review, Elsevier, vol. 81(C), pages 115-131.
    See citations under working paper version above.
  17. Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen V.K. Rombouts, 2015. "The Contribution of Structural Break Models to Forecasting Macroeconomic Series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 596-620, June.
    See citations under working paper version above.
  18. Koop, Gary & Korobilis, Dimitris, 2014. "A new index of financial conditions," European Economic Review, Elsevier, vol. 71(C), pages 101-116.
    See citations under working paper version above.
  19. Miguel A.G. Belmonte & Gary Koop & Dimitris Korobilis, 2014. "Hierarchical Shrinkage in Time‐Varying Parameter Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 80-94, January.
    See citations under working paper version above.
  20. Korobilis, Dimitris, 2013. "Hierarchical shrinkage priors for dynamic regressions with many predictors," International Journal of Forecasting, Elsevier, vol. 29(1), pages 43-59.
    See citations under working paper version above.
  21. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
    See citations under working paper version above.
  22. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    See citations under working paper version above.
  23. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, April.
    See citations under working paper version above.
  24. Korobilis, Dimitris, 2013. "Bayesian forecasting with highly correlated predictors," Economics Letters, Elsevier, vol. 118(1), pages 148-150.
    See citations under working paper version above.
  25. Michelle Gilmartin & Dimitris Korobilis, 2012. "On Regional Unemployment: An Empirical Examination of the Determinants of Geographical Differentials in the UK," Scottish Journal of Political Economy, Scottish Economic Society, vol. 59(2), pages 179-195, May.
    See citations under working paper version above.
  26. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    See citations under working paper version above.
  27. Koop, Gary & Korobilis, Dimitris, 2011. "UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?," Economic Modelling, Elsevier, vol. 28(5), pages 2307-2318, September.
    See citations under working paper version above.
  28. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    See citations under working paper version above.

Chapters

  1. Luca Gambetti & Christoph Görtz & Dimitris Korobilis & John D. Tsoukalas & Francesco Zanetti, 2022. "The Effect of News Shocks and Monetary Policy," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 139-164, Emerald Group Publishing Limited.
    See citations under working paper version above.
  2. Luc Bauwens & Dimitris Korobilis, 2013. "Bayesian methods," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 16, pages 363-380, Edward Elgar Publishing.
    See citations under working paper version above.
  3. Dimitris Korobilis, 2008. "Forecasting in vector autoregressions with many predictors," Advances in Econometrics, in: Bayesian Econometrics, pages 403-431, Emerald Group Publishing Limited.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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