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Improving forecast accuracy by combining recursive and rolling forecasts

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Cited by:

  1. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
  2. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2020. "Can systemic risk measures predict economic shocks? Evidence from China," China Economic Review, Elsevier, vol. 64(C).
  3. Craig S. Hakkio, 2008. "PCE and CPI inflation differentials: converting inflation forecasts," Economic Review, Federal Reserve Bank of Kansas City, vol. 93(Q I), pages 51-68.
  4. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
  5. Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, January.
  6. Wu, Jyh-Lin & Wang, Yi-Chiuan, 2013. "Fundamentals, forecast combinations and nominal exchange-rate predictability," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 129-145.
  7. Andersen, Torben G. & Varneskov, Rasmus T., 2022. "Testing for parameter instability and structural change in persistent predictive regressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 361-386.
  8. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
  9. Cai, Zongwu & Juhl, Ted, 2023. "The distribution of rolling regression estimators," Journal of Econometrics, Elsevier, vol. 235(2), pages 1447-1463.
  10. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
  11. Brave, Scott A. & Gascon, Charles & Kluender, William & Walstrum, Thomas, 2021. "Predicting benchmarked US state employment data in real time," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1261-1275.
  12. Liao, Xiangcheng & Mahmoud, Ali & Hu, Tiesong & Wang, Jinglin, 2022. "A novel irrigation canal scheduling model adaptable to the spatial-temporal variability of water conveyance loss," Agricultural Water Management, Elsevier, vol. 274(C).
  13. Melo, Luis F. & Loaiza, Rubén A. & Villamizar-Villegas, Mauricio, 2016. "Bayesian combination for inflation forecasts: The effects of a prior based on central banks’ estimates," Economic Systems, Elsevier, vol. 40(3), pages 387-397.
  14. Yaojie Zhang & Yudong Wang & Feng Ma & Yu Wei, 2022. "To jump or not to jump: momentum of jumps in crude oil price volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
  15. Fotis Papailias & Dimitrios Thomakos, 2015. "Covariance averaging for improved estimation and portfolio allocation," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(1), pages 31-59, February.
  16. Jianying Xie, 2021. "A New Multivariate Predictive Model for Stock Returns," Papers 2110.01873, arXiv.org.
  17. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
  18. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
  19. Todd E. Clark & Kenneth D. West, 2005. "Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference," NBER Technical Working Papers 0305, National Bureau of Economic Research, Inc.
  20. Morales-Arias, Leonardo & Moura, Guilherme V., 2013. "Adaptive forecasting of exchange rates with panel data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 493-509.
  21. M. Hashem Pesaran & Andreas Pick, 2008. "Forecasting Random Walks Under Drift Instability," CESifo Working Paper Series 2293, CESifo.
  22. Ming-Chih Lee & Chien-Liang Chiu & Wan-Hsiu Cheng, 2007. "Enhancing Forecast Accuracy By Using Long Estimation Periods," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 1(2), pages 1-9.
  23. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29, January.
  24. repec:edn:sirdps:274 is not listed on IDEAS
  25. Marie Bessec, 2010. "Étalonnages du taux de croissance du PIB français sur la base des enquêtes de conjoncture," Économie et Prévision, Programme National Persée, vol. 193(2), pages 77-99.
  26. Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
  27. Jeongwoo Kim, 2019. "Optimally adjusted last cluster for prediction based on balancing the bias and variance by bootstrapping," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-31, November.
  28. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2014. "Forecasting the oil–gasoline price relationship: Do asymmetries help?," Energy Economics, Elsevier, vol. 46(S1), pages 44-56.
  29. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
  30. Tan, Pei P. & Galagedera, Don U.A. & Maharaj, Elizabeth A., 2012. "A wavelet based investigation of long memory in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2330-2341.
  31. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.
  32. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2014. "Nowcasting GDP in Real Time: A Density Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 48-68, January.
  33. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
  34. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
  35. 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.
  36. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip, 2008. "How Much Can Outlook Forecasts be Improved? An Application to the U.S. Hog Market," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37620, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  37. Mihaela SIMIONESCU, 2014. "Improving The Inflation Rate Forecasts Of Romanian Experts Using A Fixed-Effects Models Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 13, pages 87-102, June.
  38. Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.
  39. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Staff Working Papers 07-8, Bank of Canada.
  40. Zongwu Cai & Ted Juhl, 2020. "The Distribution Of Rolling Regression Estimators," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202218, University of Kansas, Department of Economics, revised Dec 2022.
  41. Damiano B. Silipo & Giovanni Verga & Sviatlana Hlebik, 2023. "Managerial Beliefs and Banking Behavior," Journal of Financial Services Research, Springer;Western Finance Association, vol. 64(3), pages 401-431, December.
  42. Davide De Gaetano, 2018. "Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets," Mathematics, MDPI, vol. 6(3), pages 1-19, March.
  43. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
  44. João F. Caldeira & Rangan Gupta & Hudson S. Torrent, 2020. "Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value?," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
  45. 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).
  46. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
  47. Weijia Peng & Chun Yao, 2023. "Sector-level equity returns predictability with machine learning and market contagion measure," Empirical Economics, Springer, vol. 65(4), pages 1761-1798, October.
  48. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
  49. Pierre Perron & Yohei Yamamoto, 2021. "Testing for Changes in Forecasting Performance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 148-165, January.
  50. Chanont Banternghansa & Michael W. McCracken, 2011. "Real-time forecast averaging with ALFRED," Review, Federal Reserve Bank of St. Louis, vol. 93(Jan), pages 49-66.
  51. Johannes Mayr & Dirk Ulbricht, 2007. "VAR Model Averaging for Multi-Step Forecasting," ifo Working Paper Series 48, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  52. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
  53. Davide De Gaetano, 2017. "Forecasting With Garch Models Under Structural Breaks: An Approach Based On Combinations Across Estimation Windows," Departmental Working Papers of Economics - University 'Roma Tre' 0219, Department of Economics - University Roma Tre.
  54. Yaojie Zhang & Feng Ma & Chao Liang & Yi Zhang, 2021. "Good variance, bad variance, and stock return predictability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4410-4423, July.
  55. Prasad S Bhattacharya & Dimitrios D Thomakos, 2011. "Improving forecasting performance by window and model averaging," CAMA Working Papers 2011-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  56. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
  57. Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
  58. Cho, Dooyeon, 2021. "On the predictability of the distribution of excess returns in currency markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 511-530.
  59. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
  60. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1371-1390, June.
  61. Ahmed, Shamim & Liu, Xiaoquan & Valente, Giorgio, 2016. "Can currency-based risk factors help forecast exchange rates?," International Journal of Forecasting, Elsevier, vol. 32(1), pages 75-97.
  62. Norman R. Swanson & Weiqi Xiong & Xiye Yang, 2020. "Predicting interest rates using shrinkage methods, real‐time diffusion indexes, and model combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 587-613, August.
  63. Zihui Yang & Yinggang Zhou, 2017. "Quantitative Easing and Volatility Spillovers Across Countries and Asset Classes," Management Science, INFORMS, vol. 63(2), pages 333-354, February.
  64. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.
  65. Stephen G. Hall & George S. Tavlas & Yongli Wang & Deborah Gefang, 2024. "Inflation forecasting with rolling windows: An appraisal," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 827-851, July.
  66. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
  67. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
  68. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
  69. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip & Etienne, Xiaoli, 2012. "Composite and Outlook Forecast Accuracy," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), pages 1-19, August.
  70. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
  71. Graziano Moramarco, 2022. "Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers," Econometrics, MDPI, vol. 11(1), pages 1-29, December.
  72. Yuqing Feng & Yaojie Zhang & Yudong Wang, 2024. "Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 567-582, April.
  73. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2021. "Systemic risk measures and distribution forecasting of macroeconomic shocks," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 178-196.
  74. Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
  75. Rodolfo C. Moura & Márcio P. Laurini, 2021. "Spillovers and jumps in global markets: A comparative analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5997-6013, October.
  76. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
  77. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
  78. Busetti, Fabio & Marcucci, Juri, 2013. "Comparing forecast accuracy: A Monte Carlo investigation," International Journal of Forecasting, Elsevier, vol. 29(1), pages 13-27.
  79. Stavroula P. Fameliti & Vasiliki D. Skintzi, 2020. "Predictive ability and economic gains from volatility forecast combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 200-219, March.
  80. Andre Jungmittag, 2016. "Combination of Forecasts across Estimation Windows: An Application to Air Travel Demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(4), pages 373-380, July.
  81. Rubaszek Michal & Karolak Zuzanna & Kwas Marek & Uddin Gazi Salah, 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-20, December.
  82. Charles Hoffreumon & Nicolas van Zeebroeck, 2018. "Forecasting short-term transaction fees on a smart contracts platform," Working Papers TIMES² 2018-028, ULB -- Universite Libre de Bruxelles.
  83. Vellachami, Sanggetha & Hasanov, Akram Shavkatovich & Brooks, Robert, 2023. "Risk transmission from the energy markets to the carbon market: Evidence from the recursive window approach," International Review of Financial Analysis, Elsevier, vol. 89(C).
  84. Charles Ka Yui Leung, 2015. "Availability, Affordability and Volatility: The Case of the Hong Kong Housing Market," International Real Estate Review, Global Social Science Institute, vol. 18(3), pages 383-428.
  85. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
  86. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
  87. Davide De Gaetano, 2018. "Forecast Combinations for Structural Breaks in Volatility: Evidence from BRICS Countries," JRFM, MDPI, vol. 11(4), pages 1-13, October.
  88. Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying VARS," Working Papers ECARES ECARES 2015-51, ULB -- Universite Libre de Bruxelles.
  89. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
  90. Benjamin Garcia & Arsenios Skaperdas, 2017. "Inferring the Shadow Rate from Real Activity," Finance and Economics Discussion Series 2017-106, Board of Governors of the Federal Reserve System (U.S.).
  91. Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2021. "Realized skewness and the short-term predictability for aggregate stock market volatility," Economic Modelling, Elsevier, vol. 103(C).
  92. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).
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