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Complete subset regressions

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Cited by:

  1. Kourentzes, Nikolaos & Trapero, Juan R. & Barrow, Devon K., 2020. "Optimising forecasting models for inventory planning," International Journal of Production Economics, Elsevier, vol. 225(C).
  2. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
  3. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
  4. Moritz Meister & Annekatrin Niebuhr & Jan Cornelius Peters & Johannes Stiller, 2023. "Local attributes and migration balance – evidence for different age and skill groups from a machine learning approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(4), pages 794-825, May.
  5. Scholz, Michael & Nielsen, Jens Perch & Sperlich, Stefan, 2015. "Nonparametric prediction of stock returns based on yearly data: The long-term view," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 143-155.
  6. Chen, Yi-Ting & Liu, Chu-An, 2023. "Model averaging for asymptotically optimal combined forecasts," Journal of Econometrics, Elsevier, vol. 235(2), pages 592-607.
  7. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
  8. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
  9. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
  10. Antonio Gargano & Davide Pettenuzzo & Allan Timmermann, 2019. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Management Science, INFORMS, vol. 65(2), pages 508-540, February.
  11. Faias, José Afonso, 2023. "Predicting the equity risk premium using the smooth cross-sectional tail risk: The importance of correlation," Journal of Financial Markets, Elsevier, vol. 63(C).
  12. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
  13. Zhang, Yaojie & Wang, Yudong, 2023. "Forecasting crude oil futures market returns: A principal component analysis combination approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 659-673.
  14. 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.
  15. Aslanidis, Nektarios & Christiansen, Charlotte & Cipollini, Andrea, 2019. "Predicting bond betas using macro-finance variables," Finance Research Letters, Elsevier, vol. 29(C), pages 193-199.
  16. Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Jia, Zhimin, 2023. "Complete subset averaging methods in corporate bond return prediction," Finance Research Letters, Elsevier, vol. 54(C).
  17. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2023. "Commodity futures return predictability and intertemporal asset pricing," Journal of Commodity Markets, Elsevier, vol. 31(C).
  18. Kurov, Alexander & Sancetta, Alessio & Strasser, Georg & Wolfe, Marketa Halova, 2019. "Price Drift Before U.S. Macroeconomic News: Private Information about Public Announcements?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(1), pages 449-479, February.
  19. 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.
  20. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
  21. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
  22. Koo, Bonsoo & Anderson, Heather M. & Seo, Myung Hwan & Yao, Wenying, 2020. "High-dimensional predictive regression in the presence of cointegration," Journal of Econometrics, Elsevier, vol. 219(2), pages 456-477.
  23. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
  24. Scholz, Michael & Sperlich, Stefan & Nielsen, Jens Perch, 2016. "Nonparametric long term prediction of stock returns with generated bond yields," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 82-96.
  25. Dalibor Stevanovic & Rachidi Kotchoni & Maxime Leroux, 2017. "Forecasting economic activity in data-rich environment," CIRANO Working Papers 2017s-05, CIRANO.
  26. Arim Jin & Dahan Lee & Jong-Bae Park & Jae Hyung Roh, 2023. "Day-Ahead Electricity Market Price Forecasting Considering the Components of the Electricity Market Price; Using Demand Decomposition, Fuel Cost, and the Kernel Density Estimation," Energies, MDPI, vol. 16(7), pages 1-19, April.
  27. Seojeong Lee & Youngki Shin, 2021. "Complete subset averaging with many instruments," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 290-314.
  28. Boot, Tom & Nibbering, Didier, 2019. "Forecasting using random subspace methods," Journal of Econometrics, Elsevier, vol. 209(2), pages 391-406.
  29. Yanhui Chen & Ailing Feng & Shun Chen & Jackson Jinhong Mi, 2024. "Forecasting the containerized freight index with AIS data: A novel information combination method based on gray incidence analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 802-815, April.
  30. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
  31. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
  32. Beckmann, Joscha & Schüssler, Rainer, 2016. "Forecasting exchange rates under parameter and model uncertainty," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 267-288.
  33. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2015. "Complete subset regressions with large-dimensional sets of predictors," Journal of Economic Dynamics and Control, Elsevier, vol. 54(C), pages 86-110.
  34. Lee, Ji Hyung & Shin, Youngki, 2023. "Complete Subset Averaging For Quantile Regressions," Econometric Theory, Cambridge University Press, vol. 39(1), pages 146-188, February.
  35. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Annals of Economics and Statistics, GENES, issue 123-124, pages 333-361.
  36. Philippe Goulet Coulombe, 2020. "To Bag is to Prune," Papers 2008.07063, arXiv.org, revised Sep 2024.
  37. Magnus, Jan R. & Vasnev, Andrey L., 2015. "Interpretation and use of sensitivity in econometrics, illustrated with forecast combinations," International Journal of Forecasting, Elsevier, vol. 31(3), pages 769-781.
  38. Daniel Borup & Erik Christian Montes Schütte, 2022. "In Search of a Job: Forecasting Employment Growth Using Google Trends," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 186-200, January.
  39. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
  40. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
  41. Pablo Duarte & Bernd Süssmuth, 2018. "Implementing an Approximate Dynamic Factor Model to Nowcast GDP Using Sensitivity Analysis," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 127-141, April.
  42. Wang, Yunqi & Zhou, Ti, 2023. "Out-of-sample equity premium prediction: The role of option-implied constraints," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 199-226.
  43. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
  44. Wang, Yudong & Liu, Li & Ma, Feng & Diao, Xundi, 2018. "Momentum of return predictability," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 141-156.
  45. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
  46. Kartikay Gupta & Niladri Chatterjee, 2021. "Stocks Recommendation from Large Datasets Using Important Company and Economic Indicators," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 667-689, December.
  47. Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2016. "Forecasting macroeconomic variables in data-rich environments," Economics Letters, Elsevier, vol. 138(C), pages 50-52.
  48. Chrystalleni Aristidou & Kevin Lee & Kalvinder Shields, 2015. "Real-Time Data should be used in Forecasting Output Growth and Recessionary Events in the US," Discussion Papers 2015/13, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
  49. Hao, Xianfeng & Zhao, Yuyang & Wang, Yudong, 2020. "Forecasting the real prices of crude oil using robust regression models with regularization constraints," Energy Economics, Elsevier, vol. 86(C).
  50. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
  51. Jin, Sainan & Corradi, Valentina & Swanson, Norman R., 2017. "Robust Forecast Comparison," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1306-1351, December.
  52. Chu‐An Liu & Biing‐Shen Kuo, 2016. "Model averaging in predictive regressions," Econometrics Journal, Royal Economic Society, vol. 19(2), pages 203-231, June.
  53. 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.
  54. Chen, Xingyi & Li, Haiqi & Zhang, Jing, 2023. "Complete subset averaging approach for high-dimensional generalized linear models," Economics Letters, Elsevier, vol. 226(C).
  55. Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
  56. Jiun-Hua Su, 2021. "No-Regret Forecasting with Egalitarian Committees," Papers 2109.13801, arXiv.org.
  57. Buncic, Daniel & Piras, Gion Donat, 2016. "Heterogeneous agents, the financial crisis and exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 313-359.
  58. Qiu, Yue, 2021. "Complete subset least squares support vector regression," Economics Letters, Elsevier, vol. 200(C).
  59. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
  60. Matthias Pelster & Johannes Vilsmeier, 2018. "The determinants of CDS spreads: evidence from the model space," Review of Derivatives Research, Springer, vol. 21(1), pages 63-118, April.
  61. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
  62. Graham Elliott & Allan Timmermann, 2016. "Forecasting in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 81-110, October.
  63. Gargano, Antonio & Timmermann, Allan, 2014. "Forecasting commodity price indexes using macroeconomic and financial predictors," International Journal of Forecasting, Elsevier, vol. 30(3), pages 825-843.
  64. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
  65. Hongwei Zhang & Qiang He & Ben Jacobsen & Fuwei Jiang, 2020. "Forecasting stock returns with model uncertainty and parameter instability," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 629-644, August.
  66. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic forecast accuracy in a data‐rich environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.
  67. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.
  68. Cheng, Xu & Hansen, Bruce E., 2015. "Forecasting with factor-augmented regression: A frequentist model averaging approach," Journal of Econometrics, Elsevier, vol. 186(2), pages 280-293.
  69. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  70. Korobilis, Dimitris, 2018. "Machine Learning Macroeconometrics A Primer," Essex Finance Centre Working Papers 22666, University of Essex, Essex Business School.
  71. Erik Christian Montes Schütte, 2018. "In Search of a Job: Forecasting Employment Growth in the US using Google Trends," CREATES Research Papers 2018-25, Department of Economics and Business Economics, Aarhus University.
  72. Sabaj, Ernil & Kahveci, Mustafa, 2018. "Forecasting tax revenues in an emerging economy: The case of Albania," MPRA Paper 84404, University Library of Munich, Germany.
  73. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
  74. Adriano S. Koshiyama & Nikan Firoozye & Philip Treleaven, 2019. "A derivatives trading recommendation system: The mid‐curve calendar spread case," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(2), pages 83-103, April.
  75. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
  76. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Forecasting benchmarks of long-term stock returns via machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 221-240, February.
  77. David A. Mascio & Frank J. Fabozzi & J. Kenton Zumwalt, 2021. "Market timing using combined forecasts and machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 1-16, January.
  78. Olivier Fortin‐Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "A large Canadian database for macroeconomic analysis," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(4), pages 1799-1833, November.
  79. Barbara Rossi, 2019. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," Working Papers 1162, Barcelona School of Economics.
  80. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
  81. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
  82. Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
  83. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
  84. Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
  85. Zhifeng Dai & Tingyu Li & Mi Yang, 2022. "Forecasting stock return volatility: The role of shrinkage approaches in a data‐rich environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 980-996, August.
  86. DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
  87. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
  88. Sagaert, Yves R. & Aghezzaf, El-Houssaine & Kourentzes, Nikolaos & Desmet, Bram, 2018. "Tactical sales forecasting using a very large set of macroeconomic indicators," European Journal of Operational Research, Elsevier, vol. 264(2), pages 558-569.
  89. Xu Cheng & Bruce E. Hansen, 2012. "Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach, Second Version," PIER Working Paper Archive 13-061, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 03 Sep 2013.
  90. M. Chudý & S. Karmakar & W. B. Wu, 2020. "Long-term prediction intervals of economic time series," Empirical Economics, Springer, vol. 58(1), pages 191-222, January.
  91. Liu, Chu-An, 2015. "Distribution theory of the least squares averaging estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 142-159.
  92. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
  93. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2016. "Can commodity returns forecast Canadian sector stock returns?," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 172-188.
  94. 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.
  95. Wang, Yudong & Hao, Xianfeng & Wu, Chongfeng, 2021. "Forecasting stock returns: A time-dependent weighted least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
  96. Seojeong Lee & Youngki Shin, 2018. "Optimal Estimation with Complete Subsets of Instruments," Department of Economics Working Papers 2018-15, McMaster University.
  97. 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.
  98. Kourentzes, Nikolaos & Barrow, Devon & Petropoulos, Fotios, 2019. "Another look at forecast selection and combination: Evidence from forecast pooling," International Journal of Production Economics, Elsevier, vol. 209(C), pages 226-235.
  99. Brownlees, Christian & Mesters, Geert, 2021. "Detecting granular time series in large panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 544-561.
  100. Anwen Yin, 2024. "Predictive model averaging with parameter instability and heteroskedasticity," Bulletin of Economic Research, Wiley Blackwell, vol. 76(2), pages 418-442, April.
  101. 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.
  102. Anthony Garratt & Ivan Petrella, 2022. "Commodity prices and inflation risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 392-414, March.
  103. Pelster, Matthias & Vilsmeier, Johannes, 2016. "The determinants of CDS spreads: Evidence from the model space," Discussion Papers 43/2016, Deutsche Bundesbank.
  104. repec:wrk:wrkemf:23 is not listed on IDEAS
  105. Adriano Soares Koshiyama & Nick Firoozye & Philip Treleaven, 2018. "A Machine Learning-based Recommendation System for Swaptions Strategies," Papers 1810.02125, arXiv.org.
  106. Ioannis D. Vrontos & John Galakis & Ekaterini Panopoulou & Spyridon D. Vrontos, 2024. "Forecasting GDP growth: The economic impact of COVID‐19 pandemic," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1042-1086, July.
  107. 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.
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