Bagging Time Series Models

Citations

Cited by:

1. Michael McAleer & Marcelo C. Medeiros, 2009. "Forecasting Realized Volatility with Linear and Nonlinear Models," CARF F-Series CARF-F-189, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
   • Michael McAleer & Marcelo Cunha Medeiros, 2010. "Forecasting Realized Volatility with Linear and Nonlinear Models," Textos para discussão 568, Department of Economics PUC-Rio (Brazil).
   • McAleer, M.J. & Medeiros, M.C., 2009. "Forecasting Realized Volatility with Linear and Nonlinear Models," Econometric Institute Research Papers EI 2009-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
   • Michael McAleer & Marcelo C. Medeiros, 2009. "Forecasting Realized Volatility with Linear and Nonlinear Models," CIRJE F-Series CIRJE-F-686, CIRJE, Faculty of Economics, University of Tokyo.
2. Peng, Lijuan & Liang, Chao & Yang, Baoying & Wang, Lu, 2024. "Crude oil volatility forecasting: Insights from a novel time-varying parameter GARCH-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 94(C).
3. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.
   • Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," Economics Working Papers ECO2009/31, European University Institute.
   • Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.
4. Clark, Todd E. & McCracken, Michael W., 2012. "In-sample tests of predictive ability: A new approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 1-14.
   • Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Working Papers 2009-051, Federal Reserve Bank of St. Louis.
   • Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Research Working Paper RWP 09-10, Federal Reserve Bank of Kansas City.
5. Eric Hillebrand & Marcelo Cunha Medeiros, 2007. "Forecasting realized volatility models:the benefits of bagging and nonlinear specifications," Textos para discussão 547, Department of Economics PUC-Rio (Brazil).
6. Jin, Daxiang & Yu, Jize, 2023. "Predicting cryptocurrency market volatility: Novel evidence from climate policy uncertainty," Finance Research Letters, Elsevier, vol. 58(PC).
7. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
   • 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.
8. Francesco Audrino & Kameliya Filipova, 2009. "Yield Curve Predictability, Regimes, and Macroeconomic Information: A Data-Driven Approach," University of St. Gallen Department of Economics working paper series 2009 2009-10, Department of Economics, University of St. Gallen.
9. Ujjal Chatterjee, 2023. "Forecasting Economic Growth: Evidence from housing, banking, and credit conditions," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(4), pages 936-958, December.
10. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2007. "Forecasting Large Datasets with Reduced Rank Multivariate Models," Working Papers 617, Queen Mary University of London, School of Economics and Finance.
11. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
12. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
    • Ron Alquist & Lutz Kilian & Robert J. Vigfusson, 2011. "Forecasting the price of oil," International Finance Discussion Papers 1022, Board of Governors of the Federal Reserve System (U.S.).
    • Ron Alquist & Lutz Kilian & Robert Vigfusson, 2011. "Forecasting the Price of Oil," Staff Working Papers 11-15, Bank of Canada.
    • Kilian, Lutz & Alquist, Ron & Vigfusson, Robert J., 2011. "Forecasting the Price of Oil," CEPR Discussion Papers 8388, C.E.P.R. Discussion Papers.
13. Francesco Audrino & Marcelo C. Medeiros, 2008. "Smooth Regimes, Macroeconomic Variables, and Bagging for the Short-Term Interest Rate Process," University of St. Gallen Department of Economics working paper series 2008 2008-16, Department of Economics, University of St. Gallen.
14. Francisco Dias & Maximiano Pinheiro & António Rua, 2010. "Forecasting using targeted diffusion indexes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 341-352.
    • António Rua & Francisco Craveiro Dias, 2008. "Forecasting Using Targeted Diffusion Indexes," Working Papers w200807, Banco de Portugal, Economics and Research Department.
15. Chao Liang & Yi Zhang & Yaojie Zhang, 2022. "Forecasting the volatility of the German stock market: New evidence," Applied Economics, Taylor & Francis Journals, vol. 54(9), pages 1055-1070, February.
16. Eric Hillebrand & Marcelo Medeiros, 2010. "The Benefits of Bagging for Forecast Models of Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 571-593.
17. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
18. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2007. "Forecasting Large Datasets with Reduced Rank Multivariate Models," Working Papers 617, Queen Mary University of London, School of Economics and Finance.
    • Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2007. "Forecasting Large Datasets with Reduced Rank Multivariate Models," Working Papers 617, Queen Mary University of London, School of Economics and Finance.
19. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
20. Liu, Na & Gao, Fumin, 2022. "The world uncertainty index and GDP growth rate," Finance Research Letters, Elsevier, vol. 49(C).
21. Chao Liang & Yu Wei & Likun Lei & Feng Ma, 2022. "Global equity market volatility forecasting: New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 594-609, January.
22. Francesco Audrino & Marcelo C. Medeiros, 2011. "Modeling and forecasting short‐term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 999-1022, September.
    • Francesco Audrino & Marcelo Cunha Medeiros, 2010. "Modeling and Forecasting Short-term Interest Rates: The Benefits of Smooth Regimes, Macroeconomic Variables, and Bagging," Textos para discussão 570, Department of Economics PUC-Rio (Brazil).
23. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
24. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
25. Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.
26. Wang, Jiqian & Huang, Yisu & Ma, Feng & Chevallier, Julien, 2020. "Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence," Energy Economics, Elsevier, vol. 91(C).