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Generalized Shrinkage Methods for Forecasting Using Many Predictors
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- Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
- Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank.
- Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
- Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018.
"Using the Entire Yield Curve in Forecasting Output and Inflation,"
Econometrics, MDPI, vol. 6(3), pages 1-27, August.
- Tae-Hwy Lee & Eric Hillebrand & Huiyu Huang & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Working Papers 201903, University of California at Riverside, Department of Economics.
- Hillebrand, Eric & Lukas, Manuel & Wei, Wei, 2021.
"Bagging weak predictors,"
International Journal of Forecasting, Elsevier, vol. 37(1), pages 237-254.
- Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
- Eric Hillebrand & Manuel Lukas & Wei Wei, 2020. "Bagging Weak Predictors," Monash Econometrics and Business Statistics Working Papers 16/20, Monash University, Department of Econometrics and Business Statistics.
- Fornaro, Paolo, 2015. "Forecasting U.S. Recessions with a Large Set of Predictors," MPRA Paper 62973, University Library of Munich, Germany.
- 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.
- Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2011. "Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression," Tinbergen Institute Discussion Papers 11-007/4, Tinbergen Institute.
- Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2013. "Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression," CREATES Research Papers 2013-16, Department of Economics and Business Economics, Aarhus University.
- 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.
- Alessandro Giovannelli & Daniele Massacci & Stefano Soccorsi, 2020. "Forecasting Stock Returns with Large Dimensional Factor Models," Working Papers 305661169, Lancaster University Management School, Economics Department.
- Francisco Corona & Pilar Poncela & Esther Ruiz, 2017.
"Determining the number of factors after stationary univariate transformations,"
Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
- Corona, Francisco & Poncela, Maria Pilar, 2016. "Determining the number of factors after stationary univariate transformations," DES - Working Papers. Statistics and Econometrics. WS ws1602, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015.
"Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections,"
International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
- Giannone, Domenico & Bańbura, Marta & Lenza, Michele, 2014. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," Working Paper Series 1733, European Central Bank.
- Giannone, Domenico & Banbura, Marta & Lenza, Michele, 2014. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," CEPR Discussion Papers 9931, C.E.P.R. Discussion Papers.
- Martha Banbura & Domenico Giannone & Michèle Lenza, 2014. "Conditional Forecasts and Scenario Analysis with Vector Autoregressions for Large Cross-Sections," Working Papers ECARES ECARES 2014-15, ULB -- Universite Libre de Bruxelles.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2017.
"Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis,"
Journal of Econometrics, Elsevier, vol. 199(1), pages 74-92.
- Lippi, Marco & Hallin, Marc & Forni, Mario & Zaffaroni, Paolo, 2015. "Dynamic Factor Models with Infinite-Dimensional Factor Space: Asymptotic Analysis," CEPR Discussion Papers 10618, C.E.P.R. Discussion Papers.
- Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2016. "Dynamic Factor Models with Infinite-Dimensional Factor Space. Asymptotic Analysis," EIEF Working Papers Series 1607, Einaudi Institute for Economics and Finance (EIEF), revised Apr 2016.
- Pietro Dallari & Antonio Ribba, 2015. "Dynamic Factor Models with In nite-Dimensional Factor Space: Asymptotic Analysis," Center for Economic Research (RECent) 115, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
- Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2015. "Dynamic Factor Models with Infinite-Dimensional Factor Space: Asymptotic Analysis," Working Papers ECARES ECARES 2015-23, ULB -- Universite Libre de Bruxelles.
- Bodha Hannadige, Sium & Gao, Jiti & Silvapulle, Mervyn & Silvapulle, Param, 2021.
"Time Series Forecasting using a Mixture of Stationary and Nonstationary Predictors,"
MPRA Paper
108669, University Library of Munich, Germany, revised 30 Apr 2021.
- Sium Bodha Hannadige & Jiti Gao & Mervyn J Silvapulle & Param Silvapulle, 2021. "Time Series Forecasting Using a Mixture of Stationary and Nonstationary Predictors," Monash Econometrics and Business Statistics Working Papers 6/21, Monash University, Department of Econometrics and Business Statistics.
- Tommaso Proietti, 2016.
"On the Selection of Common Factors for Macroeconomic Forecasting,"
Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 593-628,
Emerald Group Publishing Limited.
- Giovannelli, Alessandro & Proietti, Tommaso, 2014. "On the Selection of Common Factors for Macroeconomic Forecasting," MPRA Paper 60673, University Library of Munich, Germany.
- Alessandro Giovannelli & Tommaso Proietti, 2014. "On the Selection of Common Factors for Macroeconomic Forecasting," CREATES Research Papers 2014-46, Department of Economics and Business Economics, Aarhus University.
- Alessandro Giovannelli & Tommaso Proietti, 2015. "On the Selection of Common Factors for Macroeconomic Forecasting," CEIS Research Paper 332, Tor Vergata University, CEIS, revised 12 Mar 2015.
- Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
- Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013.
"Now-Casting and the Real-Time Data Flow,"
Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237,
Elsevier.
- Martha Banbura & Domenico Giannone & Michèle Modugno & Lucrezia Reichlin, 2012. "Now-Casting and the Real-Time Data Flow," Working Papers ECARES ECARES 2012-026, ULB -- Universite Libre de Bruxelles.
- Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
- Giannone, Domenico & Reichlin, Lucrezia & Bańbura, Marta & Modugno, Michele, 2013. "Now-casting and the real-time data flow," Working Paper Series 1564, European Central Bank.
- Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
- Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017.
"The role of indicator selection in nowcasting euro-area GDP in pseudo-real time,"
Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
- A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
- Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023.
"LASSO principal component averaging: A fully automated approach for point forecast pooling,"
International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.
- Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019.
"Bayesian nonparametric sparse VAR models,"
Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
- Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.
- Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
- 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.
- 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.
- Ouyang, Ruolan & Pei, Tiancheng & Fang, Yi & Zhao, Yang, 2024. "Commodity systemic risk and macroeconomic predictions," Energy Economics, Elsevier, vol. 138(C).
- Norman R. Swanson & Weiqi Xiong, 2018.
"Big data analytics in economics: What have we learned so far, and where should we go from here?,"
Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
- Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics, Canadian Economics Association, vol. 51(3), pages 695-746, August.
- Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
- A. Chudik & G. Kapetanios & M. Hashem Pesaran, 2018.
"A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High‐Dimensional Linear Regression Models,"
Econometrica, Econometric Society, vol. 86(4), pages 1479-1512, July.
- Chudik, A. & Kapetanios, G. & Pesaran, Hashem, 2016. "A One-Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models," Cambridge Working Papers in Economics 1677, Faculty of Economics, University of Cambridge.
- Alexander Chudik & George Kapetanios & M. Hashem Pesaran, 2016. "A one-covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models," Globalization Institute Working Papers 290, Federal Reserve Bank of Dallas.
- Huiyu Huang & Tae-Hwy Lee, 2013.
"Forecasting Value-at-Risk Using High-Frequency Information,"
Econometrics, MDPI, vol. 1(1), pages 1-14, June.
- Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.
- Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
- Alexandra Bozhechkova & Urmat Dzhunkeev, 2024. "CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 45-69, September.
- Marine Carrasco & Barbara Rossi, 2016.
"In-Sample Inference and Forecasting in Misspecified Factor Models,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 313-338, July.
- Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
- Rossi, Barbara & Carrasco, Marine, 2016. "In-sample Inference and Forecasting in Misspecified Factor Models," CEPR Discussion Papers 11388, C.E.P.R. Discussion Papers.
- Ciner, Cetin, 2019. "Do industry returns predict the stock market? A reprise using the random forest," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 152-158.
- 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.
- Jiawen Luo & Tony Klein & Thomas Walther & Qiang Ji, 2024.
"Forecasting realized volatility of crude oil futures prices based on machine learning,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1422-1446, August.
- 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.
- Iseringhausen, Martin & Petrella, Ivan & Theodoridis, Konstantinos, 2021.
"Aggregate Skewness and the Business Cycle,"
Cardiff Economics Working Papers
E2021/30, Cardiff University, Cardiff Business School, Economics Section.
- Iseringhausen, Martin & Petrella, Ivan & Theodoridis, Konstantinos, 2022. "Aggregate Skewness and the Business Cycle," CEPR Discussion Papers 17162, C.E.P.R. Discussion Papers.
- Martin Iseringhausen & Ivan Petrella & Konstantinos Theodoridis, 2022. "Aggregate skewness and the business cycle," Working Papers 53, European Stability Mechanism.
- Marcelo C. Medeiros & Eduardo F. Mendes, 2015. "l1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations," Textos para discussão 636, Department of Economics PUC-Rio (Brazil).
- 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.
- Sun, Yuying & Hong, Yongmiao & Lee, Tae-Hwy & Wang, Shouyang & Zhang, Xinyu, 2021.
"Time-varying model averaging,"
Journal of Econometrics, Elsevier, vol. 222(2), pages 974-992.
- Yongmiao Hong & Tae-Hwy Lee & Yuying Sun & Shouyang Wang & Xinyu Zhang, 2017. "Time-varying Model Averaging," Working Papers 202001, University of California at Riverside, Department of Economics.
- Kim, Hyun Hak & Swanson, Norman R., 2014.
"Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence,"
Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
- Huyn Hak Kim & Norman R. Swanson, 2011. "Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence," Departmental Working Papers 201119, Rutgers University, Department of Economics.
- Yu, Mingzhe & Fan, Jiachuan & Wang, Haijun & Wang, Jie, 2023. "US trade policy uncertainty on Chinese agricultural imports and exports: An aggregate and product-level analysis," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 70-83.
- 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.
- Wachter, Jessica A. & Warusawitharana, Missaka, 2015.
"What is the chance that the equity premium varies over time? Evidence from regressions on the dividend-price ratio,"
Journal of Econometrics, Elsevier, vol. 186(1), pages 74-93.
- Jessica A. Wachter & Missaka Warusawitharana, 2011. "What is the Chance that the Equity Premium Varies over Time? Evidence from Regressions on the Dividend-Price Ratio," NBER Working Papers 17334, National Bureau of Economic Research, Inc.
- Eric Hillebrand & Tae-Hwy Lee & Marcelo C. Medeiros, 2012.
"Let's Do It Again: Bagging Equity Premium Predictors,"
CREATES Research Papers
2012-41, Department of Economics and Business Economics, Aarhus University.
- Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012. "Let´s do it again: bagging equity premium predictors," Textos para discussão 604, Department of Economics PUC-Rio (Brazil).
- 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.
- Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
- 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.
- Boriss Siliverstovs & Daniel Wochner, 2020. "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," Working Papers 2020/02, Latvijas Banka.
- Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.
- Boot, Tom & Nibbering, Didier, 2019.
"Forecasting using random subspace methods,"
Journal of Econometrics, Elsevier, vol. 209(2), pages 391-406.
- Tom Boot & Didier Nibbering, 2016. "Forecasting Using Random Subspace Methods," Tinbergen Institute Discussion Papers 16-073/III, Tinbergen Institute, revised 11 Aug 2017.
- Sagaert, Yves R. & Kourentzes, Nikolaos & De Vuyst, Stijn & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "Incorporating macroeconomic leading indicators in tactical capacity planning," International Journal of Production Economics, Elsevier, vol. 209(C), pages 12-19.
- Groen, Jan J.J. & Kapetanios, George, 2016.
"Revisiting useful approaches to data-rich macroeconomic forecasting,"
Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
- Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York.
- Jan J.J. Groen & George Kapetanios, 2008. "Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting," Working Papers 624, Queen Mary University of London, School of Economics and Finance.
- 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.
- 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".
- Apergis Nicholas, 2021. "Forecasting US overseas travelling with univariate and multivariate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 963-976, September.
- Esteban Fernández-Vázquez & Blanca Moreno, 2017. "Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator," Journal of Geographical Systems, Springer, vol. 19(4), pages 349-370, October.
- Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
- Ghufran Ahmad & Muhammad Suhail Rizwan & Dawood Ashraf, 2021. "Systemic risk and macroeconomic forecasting: A globally applicable copula‐based approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1420-1443, December.
- Mario Forni & Alessandro Giovannelli & Marco Lippi & Stefano Soccorsi, 2018.
"Dynamic factor model with infinite‐dimensional factor space: Forecasting,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 625-642, August.
- Forni, Mario & Giovannelli, Alessandro & Lippi, Marco & Soccorsi, Stefano, 2016. "Dynamic Factor model with infinite dimensional factor space: forecasting," CEPR Discussion Papers 11161, C.E.P.R. Discussion Papers.
- Mario Forni & Alessandro Giovannelli & Marco Lippi & Stefano Soccorsi, 2016. "Dynamic Factor Model with Infinite Dimensional Factor Space: Forecasting," Working Papers ECARES ECARES 2016-16, ULB -- Universite Libre de Bruxelles.
- Mario Forni & Alessandro Giovannelli & Marco Lippi & Stefano Soccorsi, 2016. "Dynamic Factor model with infinite dimensional factor space: forecasting," Center for Economic Research (RECent) 120, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
- Panagiotelis, Anastasios & Athanasopoulos, George & Hyndman, Rob J. & Jiang, Bin & Vahid, Farshid, 2019.
"Macroeconomic forecasting for Australia using a large number of predictors,"
International Journal of Forecasting, Elsevier, vol. 35(2), pages 616-633.
- Bin Jiang & George Athanasopoulos & Rob J Hyndman & Anastasios Panagiotelis & Farshid Vahid, 2017. "Macroeconomic forecasting for Australia using a large number of predictors," Monash Econometrics and Business Statistics Working Papers 2/17, Monash University, Department of Econometrics and Business Statistics.
- Li, Mengheng & Koopman, Siem Jan & Lit, Rutger & Petrova, Desislava, 2020. "Long-term forecasting of El Niño events via dynamic factor simulations," Journal of Econometrics, Elsevier, vol. 214(1), pages 46-66.
- repec:aei:rpaper:008586461 is not listed on IDEAS
- Smeekes, Stephan & Wijler, Etienne, 2018.
"Macroeconomic forecasting using penalized regression methods,"
International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
- Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
- Ning Xu & Jian Hong & Timothy C. G. Fisher, 2016.
"Finite-sample and asymptotic analysis of generalization ability with an application to penalized regression,"
Papers
1609.03344, arXiv.org, revised Sep 2016.
- Xu, Ning & Hong, Jian & Fisher, Timothy, 2016. "Finite-sample and asymptotic analysis of generalization ability with an application to penalized regression," MPRA Paper 73622, University Library of Munich, Germany.
- Červená, Marianna & Schneider, Martin, 2014.
"Short-term forecasting of GDP with a DSGE model augmented by monthly indicators,"
International Journal of Forecasting, Elsevier, vol. 30(3), pages 498-516.
- Marianna Cervená & Martin Schneider, 2010. "Short-term forecasting GDP with a DSGE model augmented by monthly indicators," Working Papers 163, Oesterreichische Nationalbank (Austrian Central Bank).
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023.
"Tail Forecasting With Multivariate Bayesian Additive Regression Trees,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," Working Papers 21-08R, Federal Reserve Bank of Cleveland, revised 12 Jul 2022.
- Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
- Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
- Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020.
"PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices,"
Energies, MDPI, vol. 13(14), pages 1-19, July.
- 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.
- Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
- Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
- Sergei Aliukov & Jan Buleca, 2022. "Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators," Mathematics, MDPI, vol. 10(5), pages 1-29, March.
- Ralf Brüggemann & Christian Kascha, 2017.
"Directed Graphs and Variable Selection in Large Vector Autoregressive Models,"
Working Paper Series of the Department of Economics, University of Konstanz
2017-06, Department of Economics, University of Konstanz.
- Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2018. "Directed Graphs and Variable Selection in Large Vector Autoregressive Models," Working Paper Series of the Department of Economics, University of Konstanz 2018-08, Department of Economics, University of Konstanz.
- Bertsche, Dominik & Brüggemann, Ralf & Kascha, Christian, 2019. "Directed Graph and Variable Selection in Large Vector Autoregressive Models," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203656, Verein für Socialpolitik / German Economic Association.
- James H. Stock & Mark W. Watson, 2017. "Twenty Years of Time Series Econometrics in Ten Pictures," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 59-86, Spring.
- Kascha, Christian & Trenkler, Carsten, 2015. "Forecasting VARs, model selection, and shrinkage," Working Papers 15-07, University of Mannheim, Department of Economics.
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