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Complete subset regressions. (2013). Elliott, Graham ; Timmermann, Allan ; Gargano, Antonio.
In: University of California at San Diego, Economics Working Paper Series.
RePEc:cdl:ucsdec:qt1st3n7z7.

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  1. Forecasting the containerized freight index with AIS data: A novel information combination method based on gray incidence analysis. (2024). Chen, Shun ; Feng, Ailing ; Mi, Jackson Jinhong.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:43:y:2024:i:3:p:802-815.

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  2. Time-varying forecast combination for factor-augmented regressions with smooth structural changes. (2024). Li, Haiqi ; Hong, Yongmiao ; Chen, Qitong.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:240:y:2024:i:1:s0304407624000393.

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  3. Predictive model averaging with parameter instability and heteroskedasticity. (2024). Yin, Anwen.
    In: Bulletin of Economic Research.
    RePEc:bla:buecrs:v:76:y:2024:i:2:p:418-442.

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  4. Forecasting inflation time series using score?driven dynamic models and combination methods: The case of Brazil. (2023). Lucena, Fernando Antonio ; Dias, Carlos Henrique.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:42:y:2023:i:2:p:369-401.

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  5. Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models. (2023). Gaglianone, Wagner ; Araujo, Gustavo Silva.
    In: Latin American Journal of Central Banking (previously Monetaria).
    RePEc:eee:lajcba:v:4:y:2023:i:2:s2666143823000042.

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  6. Commodity futures return predictability and intertemporal asset pricing. (2023). Poti, Valerio ; Eyiah-Donkor, Emmanuel ; Cotter, John.
    In: Journal of Commodity Markets.
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  7. Machine learning and fund characteristics help to select mutual funds with positive alpha. (2023). Gil-Bazo, Javier ; Demiguel, Victor ; Nogales, Francisco J.
    In: Journal of Financial Economics.
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  8. Nowcasting GDP with a pool of factor models and a fast estimation algorithm. (2023). Schroder, Maximilian ; Eraslan, Sercan.
    In: International Journal of Forecasting.
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  9. Targeting predictors in random forest regression. (2023). Nielsen, Mikkel Slot ; Muhlbach, Nicolaj Sondergaard ; Christensen, Bent Jesper ; Borup, Daniel.
    In: International Journal of Forecasting.
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  10. Forecasting crude oil futures market returns: A principal component analysis combination approach. (2023). Wang, Yudong ; Zhang, Yaojie.
    In: International Journal of Forecasting.
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  11. Predicting the equity risk premium using the smooth cross-sectional tail risk: The importance of correlation. (2023). Faias, Jose Afonso.
    In: Journal of Financial Markets.
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  12. Complete subset averaging methods in corporate bond return prediction. (2023). Jia, Zhimin ; Bo, Albert ; Jiang, Shan ; Cheng, Tingting.
    In: Finance Research Letters.
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  13. Out-of-sample equity premium prediction: The role of option-implied constraints. (2023). Zhou, TI ; Wang, Yunqi.
    In: Journal of Empirical Finance.
    RePEc:eee:empfin:v:70:y:2023:i:c:p:199-226.

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  14. Time-varying forecast combination for high-dimensional data. (2023). Maung, Kenwin ; Chen, Bin.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:237:y:2023:i:2:s0304407623000556.

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  15. Model averaging for asymptotically optimal combined forecasts. (2023). Liu, Chu-An ; Chen, Yi-Ting.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:235:y:2023:i:2:p:592-607.

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  16. Estimating the variance of a combined forecast: Bootstrap-based approach. (2023). Lahiri, Kajal ; Hounyo, Ulrich.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:232:y:2023:i:2:p:445-468.

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  17. Complete subset averaging approach for high-dimensional generalized linear models. (2023). Zhang, Jing ; Li, Haiqi ; Chen, Xingyi.
    In: Economics Letters.
    RePEc:eee:ecolet:v:226:y:2023:i:c:s016517652300109x.

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  18. Local attributes and migration balance – evidence for different age and skill groups from a machine learning approach. (2023). Peters, Jan Cornelius ; Stiller, Johannes ; Niebuhr, Annekatrin ; Meister, Moritz.
    In: Regional Science Policy & Practice.
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  19. Machine learning advances for time series forecasting. (2023). Mendes, Eduardo F ; Medeiros, Marcelo C ; Masini, Ricardo P.
    In: Journal of Economic Surveys.
    RePEc:bla:jecsur:v:37:y:2023:i:1:p:76-111.

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  20. Forecasting stock return volatility: The role of shrinkage approaches in a data?rich environment. (2022). Yang, MI ; Li, Tingyu ; Dai, Zhifeng.
    In: Journal of Forecasting.
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  21. Commodity prices and inflation risk. (2022). Petrella, Ivan ; Garratt, Anthony.
    In: Journal of Applied Econometrics.
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  22. A large Canadian database for macroeconomic analysis. (2022). Stevanovic, Dalibor ; Surprenant, Stephane ; Leroux, Maxime ; Fortingagnon, Olivier.
    In: Canadian Journal of Economics/Revue canadienne d'économique.
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  23. Big data forecasting of South African inflation. (2022). Steenkamp, Daan ; Rankin, Neil ; Kotze, Kevin ; Botha, Byron ; Burger, Rulof.
    In: Working Papers.
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  24. Forecasting crude oil market returns: Enhanced moving average technical indicators. (2022). Zhang, Yaojie ; Wang, Yudong ; Liu, LI ; Wen, Danyan.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:76:y:2022:i:c:s0301420722000216.

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  25. Stock return prediction: Stacking a variety of models. (2022). Cheng, Tingting ; Bo, Albert.
    In: Journal of Empirical Finance.
    RePEc:eee:empfin:v:67:y:2022:i:c:p:288-317.

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  26. Nowcasting GDP using machine learning methods. (2022). Kant, Dennis ; Pick, Andreas ; de Winter, Jasper.
    In: Working Papers.
    RePEc:dnb:dnbwpp:754.

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  27. Machine Learning Methods for Inflation Forecasting in Brazil: new contenders versus classical models. (2022). Gaglianone, Wagner ; Araujo, Gustavo Silva.
    In: Working Papers Series.
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  29. .

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  30. Forecasting US stock market volatility: How to use international volatility information. (2021). Ma, Feng ; Wang, Yudong ; Zhang, Yaojie.
    In: Journal of Forecasting.
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  31. Market timing using combined forecasts and machine learning. (2021). Fabozzi, Frank J ; Mascio, David A ; Zumwalt, Kenton J.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:40:y:2021:i:1:p:1-16.

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  32. Can machine learning help to select portfolios of mutual funds?. (2021). Gil-Bazo, Javier ; Nogales, Francisco J ; Demiguel, Victor ; de Miguel, Victor .
    In: Economics Working Papers.
    RePEc:upf:upfgen:1772.

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  33. Forecasting benchmarks of long-term stock returns via machine learning. (2021). Scholz, Michael ; Nielsen, Jens Perch ; Mousavi, Parastoo ; Kyriakou, Ioannis.
    In: Annals of Operations Research.
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  34. Stocks Recommendation from Large Datasets Using Important Company and Economic Indicators. (2021). Chatterjee, Niladri ; Gupta, Kartikay.
    In: Asia-Pacific Financial Markets.
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  35. Macroeconomic data transformations matter. (2021). Stevanovic, Dalibor ; Surprenant, Stephane ; Leroux, Maxime ; Coulombe, Philippe Goulet.
    In: International Journal of Forecasting.
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  36. Forecasting stock returns: A time-dependent weighted least squares approach. (2021). Wang, Yudong ; Wu, Chongfeng ; Hao, Xianfeng.
    In: Journal of Financial Markets.
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  37. Machine learning and oil price point and density forecasting. (2021). Issler, João ; Gaglianone, Wagner ; Cavalcanti, Pedro ; Bonnet, Alexandre ; Lin, Yihao ; Teixeira, Osmani.
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  38. Complete subset least squares support vector regression. (2021). Qiu, Yue.
    In: Economics Letters.
    RePEc:eee:ecolet:v:200:y:2021:i:c:s0165176521000148.

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  39. Predicting risk in energy markets: Low-frequency data still matter. (2021). Výrost, Tomᚠ; Vrost, Toma ; Todorova, Neda ; Lyocsa, Tefan.
    In: Applied Energy.
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  40. Can Machine Learning Help to Select Portfolios of Mutual Funds?. (2021). , Andre ; Nogales, Francisco J ; Gil-Bazo, Javier ; Demiguel, Victor ; de Miguel, Victor .
    In: Working Papers.
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  41. Machine Learning and Oil Price Point and Density Forecasting. (2021). Gaglianone, Wagner ; Lin, Yihao ; Issler, Joo Victor ; Teixeira, Osmani ; Cavalcanti, Pedro ; Bonnet, Alexandre.
    In: Working Papers Series.
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  42. Estimating the Variance of a Combined Forecast: Bootstrap-Based Approach. (2021). Lahiri, Kajal ; Hounyo, Ulrich.
    In: CREATES Research Papers.
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  43. Composite likelihood methods for large Bayesian VARs with stochastic volatility. (2020). Koop, Gary ; Chan, Joshua ; Hou, Chenghan ; Eisenstat, Eric.
    In: Journal of Applied Econometrics.
    RePEc:wly:japmet:v:35:y:2020:i:6:p:692-711.

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  44. Forecasting stock returns with model uncertainty and parameter instability. (2020). Zhang, Hongwei ; Jiang, Fuwei ; Jacobsen, Ben ; He, Qiang.
    In: Journal of Applied Econometrics.
    RePEc:wly:japmet:v:35:y:2020:i:5:p:629-644.

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  45. Commodity Futures Return Predictability and Intertemporal Asset Pricing. (2020). Eyiah-Donkor, Emmanuel ; Cotter, John ; Pot, Valerio.
    In: Working Papers.
    RePEc:ucd:wpaper:202011.

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  46. Long-term prediction intervals of economic time series. (2020). Chud, M ; Wu, W B ; Karmakar, S.
    In: Empirical Economics.
    RePEc:spr:empeco:v:58:y:2020:i:1:d:10.1007_s00181-019-01689-2.

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  47. Complete Subset Averaging for Quantile Regressions. (2020). Shin, Youngki ; Lee, Ji Hyung.
    In: Department of Economics Working Papers.
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  48. Optimising forecasting models for inventory planning. (2020). Barrow, Devon K ; Trapero, Juan R ; Kourentzes, Nikolaos.
    In: International Journal of Production Economics.
    RePEc:eee:proeco:v:225:y:2020:i:c:s0925527319304323.

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  49. Forecasting the real prices of crude oil using robust regression models with regularization constraints. (2020). Wang, Yudong ; Hao, Xianfeng ; Zhao, Yuyang.
    In: Energy Economics.
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  50. High-dimensional predictive regression in the presence of cointegration. (2020). Anderson, Heather ; Yao, Wenying ; Seo, Myung Hwan ; Koo, Bonsoo.
    In: Journal of Econometrics.
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  51. Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them. (2020). Rossi, Barbara.
    In: CEPR Discussion Papers.
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  52. Macroeconomic Data Transformations Matter. (2020). Stevanovic, Dalibor ; Surprenant, Stephane ; Leroux, Maxime ; Coulombe, Philippe Goulet.
    In: CIRANO Working Papers.
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  53. Proyección de la Inflación en Chile con Métodos de Machine Learning. (2020). Zilberman, Eduardo ; Molina, Carlos ; Leal, Felipe.
    In: Working Papers Central Bank of Chile.
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  54. Machine Learning Advances for Time Series Forecasting. (2020). Mendes, Eduardo F ; Medeiros, Marcelo C ; Masini, Ricardo P.
    In: Papers.
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  55. High Dimensional Forecast Combinations Under Latent Structures. (2020). Su, Liangjun ; Shi, Zhentao ; Xie, Tian.
    In: Papers.
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  56. To Bag is to Prune. (2020). Coulombe, Philippe Goulet.
    In: Papers.
    RePEc:arx:papers:2008.07063.

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  57. Macroeconomic Data Transformations Matter. (2020). Stevanovic, Dalibor ; Surprenant, St'Ephane ; Leroux, Maxime ; Coulombe, Philippe Goulet.
    In: Papers.
    RePEc:arx:papers:2008.01714.

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  58. Complete Subset Averaging for Quantile Regressions. (2020). Shin, Youngki ; Lee, Ji Hyung.
    In: Papers.
    RePEc:arx:papers:2003.03299.

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  59. Targeting predictors in random forest regression. (2020). Nielsen, Mikkel S ; Muhlbach, Nicolaj N ; Christensen, Bent Jesper ; Borup, Daniel.
    In: CREATES Research Papers.
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  60. Commodity Prices and Inflation Risk. (2019). Petrella, Ivan ; Garratt, Anthony.
    In: EMF Research Papers.
    RePEc:wrk:wrkemf:23.

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  61. Macroeconomic forecast accuracy in a data‐rich environment. (2019). Stevanovic, Dalibor ; Kotchoni, Rachidi ; Leroux, Maxime.
    In: Journal of Applied Econometrics.
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  62. A derivatives trading recommendation system: The mid‐curve calendar spread case. (2019). Koshiyama, Adriano S ; Treleaven, Philip ; Firoozye, Nikan.
    In: Intelligent Systems in Accounting, Finance and Management.
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  63. Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them. (2019). Rossi, Barbara.
    In: Economics Working Papers.
    RePEc:upf:upfgen:1711.

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  64. Bond Return Predictability: Economic Value and Links to the Macroeconomy. (2019). Pettenuzzo, Davide ; Timmermann, Allan ; Gargano, Antonio.
    In: Management Science.
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  65. Another look at forecast selection and combination: Evidence from forecast pooling. (2019). Petropoulos, Fotios ; Barrow, Devon ; Kourentzes, Nikolaos.
    In: International Journal of Production Economics.
    RePEc:eee:proeco:v:209:y:2019:i:c:p:226-235.

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  66. Combining wavelet decomposition with machine learning to forecast gold returns. (2019). Risse, Marian.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:35:y:2019:i:2:p:601-615.

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  67. Predicting bond betas using macro-finance variables. (2019). Christiansen, Charlotte ; Cipollini, Andrea ; Aslanidis, Nektarios.
    In: Finance Research Letters.
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  68. Forecasting oil price volatility: Forecast combination versus shrinkage method. (2019). Wei, YU ; Zhang, Yaojie ; Jin, Daxiang.
    In: Energy Economics.
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  69. Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?. (2019). Wang, Yudong ; Ma, Feng ; Zhang, Yaojie.
    In: Journal of Empirical Finance.
    RePEc:eee:empfin:v:54:y:2019:i:c:p:97-117.

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  70. Bayesian compressed vector autoregressions. (2019). Pettenuzzo, Davide ; Korobilis, Dimitris ; Koop, Gary.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:210:y:2019:i:1:p:135-154.

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  71. Forecasting using random subspace methods. (2019). Nibbering, Didier ; Boot, Tom .
    In: Journal of Econometrics.
    RePEc:eee:econom:v:209:y:2019:i:2:p:391-406.

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  72. Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them. (2019). Rossi, Barbara.
    In: Working Papers.
    RePEc:bge:wpaper:1162.

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  73. Complete Subset Averaging with Many Instruments. (2019). Shin, Youngki ; Lee, Seojeong.
    In: Papers.
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  74. In search of a job: Forecasting employment growth using Google Trends. (2019). Montes, Erik Christian ; Borup, Daniel.
    In: CREATES Research Papers.
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  75. Implementing an approximate dynamic factor model to nowcast GDP using sensitivity analysis. (2018). Duarte, Pablo ; Sussmuth, Bernd.
    In: Working Papers.
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  76. Predicting Bond Betas using Macro-Finance Variables. (2018). cipollini, andrea ; Christiansen, Charlotte ; Aslanidis, Nektarios.
    In: Working Papers.
    RePEc:urv:wpaper:2072/306546.

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  77. Machine Learning Macroeconometrics: A Primer. (2018). Korobilis, Dimitris.
    In: Working Paper series.
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  78. Forecasting tax revenues in an emerging economy: The case of Albania. (2018). Sabaj, Ernil ; Kahveci, Mustafa .
    In: MPRA Paper.
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  79. Optimal Estimation with Complete Subsets of Instruments. (2018). Shin, Youngki ; Lee, Seojeong.
    In: Department of Economics Working Papers.
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  80. The determinants of CDS spreads: evidence from the model space. (2018). Vilsmeier, Johannes ; Pelster, Matthias.
    In: Review of Derivatives Research.
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  81. Momentum of return predictability. (2018). Wang, Yudong ; Diao, Xundi ; Ma, Feng ; Liu, LI.
    In: Journal of Empirical Finance.
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  82. Tactical sales forecasting using a very large set of macroeconomic indicators. (2018). Aghezzaf, El-Houssaine ; Desmet, Bram ; Sagaert, Yves R ; Kourentzes, Nikolaos.
    In: European Journal of Operational Research.
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  83. Forecasting Methods in Finance. (2018). Timmermann, Allan G.
    In: CEPR Discussion Papers.
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  84. A Large Canadian Database for Macroeconomic Analysis. (2018). Stevanovic, Dalibor ; Surprenant, Stephane ; Leroux, Maxime ; Fortin-Gagnon, Olivier.
    In: CIRANO Working Papers.
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  85. A Machine Learning-based Recommendation System for Swaptions Strategies. (2018). Treleaven, Philip ; Firoozye, Nick ; Koshiyama, Adriano Soares.
    In: Papers.
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  86. Large-Scale Dynamic Predictive Regressions. (2018). Bianchi, Daniele ; McAlinn, Kenichiro.
    In: Papers.
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  87. Forecasting Methods in Finance. (2018). Timmermann, Allan.
    In: Annual Review of Financial Economics.
    RePEc:anr:refeco:v:10:y:2018:p:449-479.

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  88. In Search of a Job: Forecasting Employment Growth in the US using Google Trends. (2018). Montes, Erik Christian.
    In: CREATES Research Papers.
    RePEc:aah:create:2018-25.

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  89. Forecasting Using Random Subspace Methods. (2017). Boot, Tom ; Nibbering, Didier .
    In: Tinbergen Institute Discussion Papers.
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  90. A Machine Learning Approach to the Forecast Combination Puzzle. (2017). Mandel, Antoine ; Sani, Amir.
    In: Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers).
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  91. Real-time inflation forecasting with high-dimensional models: The case of Brazil. (2017). Medeiros, Marcelo.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:33:y:2017:i:3:p:679-693.

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  92. Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market. (2017). Risse, Marian ; Ohl, Ludwig.
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