IDEAS home Printed from https://ideas.repec.org/f/c/pst385.html
   My authors  Follow this author

Dalibor Stevanovic

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Jean Boivin & Marc Giannoni & Dalibor Stevanovic, 2013. "Dynamic effects of credit shocks in a data-rich environment," Staff Reports 615, Federal Reserve Bank of New York.

    Mentioned in:

    1. > Econometrics > Time Series Models > Dynamic Factor Models > Structural Factor Models

Working papers

  1. Kevin Moran & Dalibor Stevanovic & Adam Abdel Kader Touré, 2023. "Confiance et activité économique : analyse d’impact sur l’économie canadienne," CIRANO Project Reports 2023rp-10, CIRANO.

    Cited by:

    1. Kevin Moran & Dalibor Stevanovic & Stephane Surprenant, 2024. "Risk Scenarios and Macroeconomic Forecasts," Working Papers 24-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised May 2025.
    2. Kevin Moran & Dalibor Stevanovic & Stéphane Surprenant, 2024. "Risk Scenarios and Macroeconomic Impacts: Insights for Canadian Policy," CIRANO Working Papers 2024s-03, CIRANO.

  2. Massimiliano Marcellino & Dalibor Stevanovic, 2022. "The demand and supply of information about inflation," Working Papers 22-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2022.

    Cited by:

    1. David Ardia & Keven Bluteau, 2024. "Optimal Text-Based Time-Series Indices," Papers 2405.10449, arXiv.org.

  3. Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," Papers 2103.01201, arXiv.org.

    Cited by:

    1. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    2. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    3. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Papers 2008.01714, arXiv.org, revised Mar 2021.
    4. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Papers 2103.01926, arXiv.org, revised Jul 2021.
      • Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Working Papers 21-02, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    5. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    6. Niko Hauzenberger & Florian Huber & Karin Klieber, 2020. "Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques," Papers 2012.08155, arXiv.org, revised Dec 2021.
    7. 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.
    8. James Chapman & Ajit Desai, 2022. "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers 22-10, Bank of Canada.
    9. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.
    10. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    11. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    12. 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.
    13. Ying Lun Cheung, 2024. "Identification of Time-Varying Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 76-94, January.

  4. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "A Large Canadian Database for Macroeconomic Analysis," Working Papers 20-07, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.

    Cited by:

    1. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    2. Andrea A. Naghi & Eoghan O'Neill & Martina Danielova Zaharieva, 2024. "The benefits of forecasting inflation with machine learning: New evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1321-1331, November.
    3. Xin Sheng & Rangan Gupta & Oguzhan Cepni, 2023. "Time-Varying Effects of Extreme Weather Shocks on Output Growth of the United States," Working Papers 202324, University of Pretoria, Department of Economics.
    4. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    5. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    6. Kevin Moran & Adam Abdel Kader Touré & Dalibor Stevanovic, 2020. "Incertitude et effets macroéconomiques : mise à jour dans le contexte de la pandémie COVID-19," CIRANO Papers 2020pe-33, CIRANO.
    7. Ardia, David & Bluteau, Keven & Kassem, Alaa, 2021. "A century of Economic Policy Uncertainty through the French–Canadian lens," Economics Letters, Elsevier, vol. 205(C).
    8. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
    9. Manuel Paquette-Dupuis & Dalibor Stevanovic & Rachidi Kotchoni, 2019. "Prévisions de l’activité économique en temps de crise," CIRANO Project Reports 2019rp-04, CIRANO.
    10. Julien Martin & Kevin Moran & Dalibor Stevanovic, 2025. "Macroeconomic Impacts of a Canada-U.S. Tariff War," CIRANO Papers 2025pr-04, CIRANO.
    11. Tuzcuoglu, Kerem, 2024. "Nonlinear transmission of international financial stress," Economic Modelling, Elsevier, vol. 139(C).
    12. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    13. Julien Champagne & Guillaume Poulin-Bellisle & Rodrigo Sekkel, 2018. "Evaluating the Bank of Canada Staff Economic Projections Using a New Database of Real-Time Data and Forecasts," Staff Working Papers 18-52, Bank of Canada.
    14. Kevin Moran & Dalibor Stevanovic & Stephane Surprenant, 2024. "Risk Scenarios and Macroeconomic Forecasts," Working Papers 24-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised May 2025.
    15. Kevin Moran & Dalibor Stevanovic & Stéphane Surprenant, 2024. "Risk Scenarios and Macroeconomic Impacts: Insights for Canadian Policy," CIRANO Working Papers 2024s-03, CIRANO.
    16. Matteo Barigozzi & Claudio Lissona & Lorenzo Tonni, 2024. "Large datasets for the Euro Area and its member countries and the dynamic effects of the common monetary policy," Papers 2410.05082, arXiv.org.

  5. Kevin Moran & Dalibor Stevanovic & Adam Kader Toure, 2020. "Macroeconomic Uncertainty and the COVID-19 Pandemic: Measure and Impacts on the Canadian Economy," Working Papers 20-18, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Dec 2020.

    Cited by:

    1. Masayuki MORIKAWA, 2022. "Firms' Knightian Uncertainty during the COVID-19 Crisis," Discussion papers 22089, Research Institute of Economy, Trade and Industry (RIETI).
    2. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    3. Serena Ng, 2021. "Modeling Macroeconomic Variations After COVID-19," Papers 2103.02732, arXiv.org, revised Jul 2021.
    4. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.
    5. Josué Diwambuena & Jean-Paul K. Tsasa, 2021. "The Real Effects of Uncertainty Shocks: New Evidence from Linear and Nonlinear SVAR Models," BEMPS - Bozen Economics & Management Paper Series BEMPS87, Faculty of Economics and Management at the Free University of Bozen.
    6. Pegah Derakhshan & William C. Miller & Jaimie Borisoff & Elham Esfandiari & Sue Forwell & Tal Jarus & Somayyeh Mohammadi & Isabelle Rash & Brodie Sakakibara & Julia Schmidt & Gordon Tao & Noah Tregobo, 2022. "Describing the Function, Disability, and Health of Adults and Older Adults during the Early Coronavirus Restrictions in 2019: An Online Survey," Disabilities, MDPI, vol. 2(4), pages 1-13, September.
    7. Kevin Moran & Dalibor Stevanovic & Stephane Surprenant, 2024. "Risk Scenarios and Macroeconomic Forecasts," Working Papers 24-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised May 2025.
    8. Masayuki MORIKAWA, 2022. "Uncertainty of Firms' Medium-term Outlook during the COVID-19 Pandemic," Discussion papers 22079, Research Institute of Economy, Trade and Industry (RIETI).
    9. Kevin Moran & Dalibor Stevanovic & Stéphane Surprenant, 2024. "Risk Scenarios and Macroeconomic Impacts: Insights for Canadian Policy," CIRANO Working Papers 2024s-03, CIRANO.
    10. Masayuki MORIKAWA, 2023. "Price Setting of Firms under Cost Uncertainty," Discussion papers 23040, Research Institute of Economy, Trade and Industry (RIETI).

  6. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Papers 2008.01714, arXiv.org, revised Mar 2021.

    Cited by:

    1. Miquel Oliu-Barton & Bary S R Pradelski & Nicolas Woloszko & Lionel Guetta-Jeanrenaud & Philippe Aghion & Patrick Artus & Arnaud Fontanet & Philippe Martin & Guntram B Wolff, 2022. "The Effect of COVID Certificates on Vaccine Uptake, Health Outcomes, and the Economy," SciencePo Working papers Main hal-03813557, HAL.
    2. Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
    3. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    4. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.
    5. Donato Ceci & Andrea Silvestrini, 2023. "Nowcasting the state of the Italian economy: The role of financial markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1569-1593, November.
    6. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Papers 2103.01926, arXiv.org, revised Jul 2021.
      • Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Working Papers 21-02, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    7. Katalin Varga & Tibor Szendrei, 2024. "Non-stationary Financial Risk Factors and Macroeconomic Vulnerability for the UK," Papers 2404.01451, arXiv.org.
    8. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    9. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    10. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
    11. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
    12. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    13. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    14. Robert-Paul Berben & Rajni Rasiawan & Jasper de Winter, 2025. "Forecasting Dutch inflation using machine learning methods," Working Papers 828, DNB.
    15. Lily Davies & Mark Kattenberg & Benedikt Vogt, 2023. "Predicting Firm Exits with Machine Learning: Implications for Selection into COVID-19 Support and Productivity Growth," CPB Discussion Paper 444, CPB Netherlands Bureau for Economic Policy Analysis.
    16. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
    17. 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.
    18. Jairo Flores & Bruno Gonzaga & Walter Ruelas-Huanca & Juan Tang, 2025. "Nowcasting Peru's GDP with Machine Learning Methods," IHEID Working Papers 01-2025, Economics Section, The Graduate Institute of International Studies.
    19. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.

  7. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.

    Cited by:

    1. Baruník, Jozef & Hanus, Luboš, 2024. "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, vol. 61(C).
    2. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2024. "Lessons from nowcasting GDP across the world," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 8, pages 187-217, Edward Elgar Publishing.
    3. Barbara Rossi, 2021. "Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them," Journal of Economic Literature, American Economic Association, vol. 59(4), pages 1135-1190, December.
    4. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2022. "Testing big data in a big crisis: Nowcasting under COVID-19," JRC Working Papers in Economics and Finance 2022-06, Joint Research Centre, European Commission.
    5. Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
    6. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    7. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    8. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
    9. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
    10. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Papers 2008.01714, arXiv.org, revised Mar 2021.
    11. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
    12. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    13. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
    14. Shovon Sengupta & Bhanu Pratap & Amit Pawar, 2025. "Non-linear Phillips Curve for India: Evidence from Explainable Machine Learning," Papers 2504.05350, arXiv.org.
    15. Maehashi, Kohei & Shintani, Mototsugu, 2020. "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    16. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
    17. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    18. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2024. "Nowcasting consumer price inflation using high-frequency scanner data: evidence from Germany," Working Paper Series 2930, European Central Bank.
    19. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," CEPR Discussion Papers 18298, C.E.P.R. Discussion Papers.
    20. Emilio Colombo & Matteo Pelagatti, 2019. "Statistical Learning and Exchange Rate Forecasting," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis1901, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    21. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
    22. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
    23. Silva, Thiago Christiano & Wilhelm, Paulo Victor Berri & Amancio, Diego R., 2024. "Machine learning and economic forecasting: The role of international trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 649(C).
    24. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    25. Sihan Tu & Zhaoxing Gao, 2025. "A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting," Papers 2502.15275, arXiv.org.
    26. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.
    27. Niko Hauzenberger & Florian Huber & Karin Klieber, 2020. "Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques," Papers 2012.08155, arXiv.org, revised Dec 2021.
    28. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
    29. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    30. 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.
    31. Ademmer, Martin & Beckmann, Joscha & Bode, Eckhardt & Boysen-Hogrefe, Jens & Funke, Manuel & Hauber, Philipp & Heidland, Tobias & Hinz, Julian & Jannsen, Nils & Kooths, Stefan & Söder, Mareike & Stame, 2021. "Big Data in der makroökonomischen Analyse," Kieler Beiträge zur Wirtschaftspolitik 32, Kiel Institute for the World Economy (IfW Kiel).
    32. Mihail Yanchev, 2025. "Interval, Quantile and Density Forecasts," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 1, pages 109-129, March.
    33. James Chapman & Ajit Desai, 2022. "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers 22-10, Bank of Canada.
    34. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    35. Robert-Paul Berben & Rajni Rasiawan & Jasper de Winter, 2025. "Forecasting Dutch inflation using machine learning methods," Working Papers 828, DNB.
    36. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.
    37. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
    38. Francis X. Diebold & Maximilian Goebel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," Papers 2206.10721, arXiv.org, revised Jun 2023.
    39. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    40. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
    41. Bas Scheer, 2022. "Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle," CPB Discussion Paper 434, CPB Netherlands Bureau for Economic Policy Analysis.
    42. Julian Ashwin & Eleni Kalamara & Lorena Saiz, 2024. "Nowcasting Euro area GDP with news sentiment: A tale of two crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 887-905, August.
    43. Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
    44. Jihad El Hokayem & Ibrahim Jamali & Ale Hejase, 2024. "A forecasting model for oil prices using a large set of economic indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1615-1624, August.
    45. Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
    46. Duan, Yuejiao & Goodell, John W. & Li, Haoran & Li, Xinming, 2022. "Assessing machine learning for forecasting economic risk: Evidence from an expanded Chinese financial information set," Finance Research Letters, Elsevier, vol. 46(PA).
    47. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
    48. Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
    49. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    50. van Dijk Herman K., 2024. "Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 155-176, April.
    51. Kevin Moran & Dalibor Stevanovic & Adam Abdel Kader Touré, 2020. "Macroeconomic Uncertainty and the COVID-19 Pandemic: Measure and Impacts on the Canadian Economy," CIRANO Working Papers 2020s-47, CIRANO.
    52. Nicolas Gavoille, 2025. "A short drop or a sudden stop? Sanctions, trade shocks, and firms' adjustment margins," Working Papers 2025/03, Latvijas Banka.
    53. Ajit Desai, 2023. "Machine learning for economics research: when, what and how," Staff Analytical Notes 2023-16, Bank of Canada.
    54. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
    55. Varlam Kutateladze, 2021. "The Kernel Trick for Nonlinear Factor Modeling," Papers 2103.01266, arXiv.org.
    56. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
    57. 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.
    58. Adämmer, Philipp & Prüser, Jan & Schüssler, Rainer A., 2025. "Forecasting macroeconomic tail risk in real time: Do textual data add value?," International Journal of Forecasting, Elsevier, vol. 41(1), pages 307-320.
    59. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
    60. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    61. Zhang, Hongwei & Wang, Wentao & Niu, Zibo, 2024. "Geopolitical risks and crude oil futures volatility: Evidence from machine learning," Resources Policy, Elsevier, vol. 98(C).
    62. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "Econometrics of machine learning methods in economic forecasting," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 10, pages 246-273, Edward Elgar Publishing.
    63. 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.
    64. Yucheng Yang & Yue Pang & Guanhua Huang & Weinan E, 2020. "The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data," Papers 2010.05172, arXiv.org.
    65. McWilliams, William N. & Isengildina Massa, Olga & Stewart, Shamar L., 2024. "Annual Food Price Inflation Forecasting: A Macroeconomic Random Forest Approach," 2024 Annual Meeting, July 28-30, New Orleans, LA 343923, Agricultural and Applied Economics Association.
    66. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    67. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
    68. 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.
    69. 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.
    70. Pijush Kanti Das & Prabir Kumar Das, 2024. "Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(2), pages 493-517, June.
    71. Jairo Flores & Bruno Gonzaga & Walter Ruelas-Huanca & Juan Tang, 2025. "Nowcasting Peru's GDP with Machine Learning Methods," IHEID Working Papers 01-2025, Economics Section, The Graduate Institute of International Studies.
    72. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    73. Rajveer Jat & Daanish Padha, 2024. "Kernel Three Pass Regression Filter," Papers 2405.07292, arXiv.org, revised Feb 2025.
    74. Dewang Li & Chingfei Luo & Meilan Qiu, 2025. "Optimal Weighted Markov Model and Markov Optimal Weighted Combination Model with Their Application in Hunan’s Gross Domestic Product," Mathematics, MDPI, vol. 13(3), pages 1-19, February.
    75. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
    76. Dangxing Chen & Luyao Zhang, 2023. "Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance," Papers 2301.07060, arXiv.org.
    77. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
    78. Clément Cariou & Amélie Charles & Olivier Darné, 2024. "Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2341-2357, September.
    79. Florian Huber & Massimiliano Marcellino, 2023. "Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification," Papers 2304.07856, arXiv.org, revised May 2023.
    80. Muhammad Anees Khan & Kumail Abbas & Mazliham Mohd Su’ud & Anas A. Salameh & Muhammad Mansoor Alam & Nida Aman & Mehreen Mehreen & Amin Jan & Nik Alif Amri Bin Nik Hashim & Roslizawati Che Aziz, 2022. "Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    81. Jozef Baruník & Luboš Hanus, 2025. "Taming Data‐Driven Probability Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 676-691, March.
    82. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2024. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Post-Print hal-05056934, HAL.
    83. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    84. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019. "Machine Learning for Forecasting Excess Stock Returns The Five-Year-View," Graz Economics Papers 2019-06, University of Graz, Department of Economics.
    85. Johan Brannlund & Helen Lao & Maureen MacIsaac & Jing Yang, 2023. "Predicting Changes in Canadian Housing Markets with Machine Learning," Discussion Papers 2023-21, Bank of Canada.
    86. Sakai Ando & Taehoon Kim, 2024. "Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(4), pages 1386-1410, December.
    87. Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021. "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers 446, Institute of Economic Growth.
    88. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
    89. Berigel, Muhammet & Boztaş, Gizem Dilan & Rocca, Antonella & Neagu, Gabriela, 2024. "Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    90. Midha, Joshua, 2024. "Assessing Emerging Markets through Transactional Dynamics: A New Multi-Dimensional Valuation Framework," SocArXiv d8jkt, Center for Open Science.
    91. Iva Glišic, 2024. "A comparison of using MIDAS and LSTM models for GDP nowcasting," Working Papers Bulletin 22, National Bank of Serbia.
    92. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    93. Moffo, Ahmadou Mustapha Fonton, 2024. "A machine learning approach in stress testing US bank holding companies," International Review of Financial Analysis, Elsevier, vol. 95(PC).
    94. Pietro Bogani & Matteo Fontana & Luca Neri & Simone Vantini, 2024. "Calibrated quantile prediction for Growth-at-Risk," Papers 2411.00520, arXiv.org.
    95. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    96. 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.
    97. Alain Dudoit & Molivann Panot & Thierry Warin, 2021. "Towards a multi-stakeholder Intermodal Trade-Transportation Data-Sharing and Knowledge Exchange Network," CIRANO Project Reports 2021rp-28, CIRANO.

  8. Claudia Foroni & Massimiliano Marcellino & Dalibor Stevanovic, 2020. "Forecasting the COVID-19 recession and recovery: Lessons from the financial crisis," Working Papers 20-14, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2020.

    Cited by:

    1. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    2. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    3. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    4. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    5. Richard B. Freeman, 2022. "Planning for the “Expected Unexpected”: Work and Retirement in the U.S. After the COVID-19 Pandemic Shock," NBER Working Papers 29653, National Bureau of Economic Research, Inc.
    6. Teng, Bin & Wang, Sicong & Shi, Yufeng & Sun, Yunchuan & Wang, Wei & Hu, Wentao & Shi, Chaojun, 2022. "Economic recovery forecasts under impacts of COVID-19," Economic Modelling, Elsevier, vol. 110(C).
    7. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    8. Rybacki, Jakub & Gniazdowski, Michał, 2021. "Macroeconomic Forecasting in Poland: Lessons From the COVID-19 Outbreak," MPRA Paper 107682, University Library of Munich, Germany.
    9. Suckert, Lisa, 2021. "Von der Pandemie zu einer Neuordnung der Zeit? Zeitsoziologische Perspektiven auf das Verhältnis von Zeitlichkeit, Wirtschaft und Staat," MPIfG Discussion Paper 21/7, Max Planck Institute for the Study of Societies.
    10. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
    11. Lorenzo Fratoni & Susanna Levantesi & Massimiliano Menzietti, 2022. "Measuring Financial Sustainability and Social Adequacy of the Italian NDC Pension System under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    12. Serena Ng, 2021. "Modeling Macroeconomic Variations After COVID-19," Papers 2103.02732, arXiv.org, revised Jul 2021.
    13. Yose Rizal Damuri & Prabaning Tyas & Haryo Aswicahyono & Lionel Priyadi & Stella Kusumawardhani & Ega Kurnia Yazid, 2021. "Tracking the Ups and Downs in Indonesia’s Economic Activity During COVID-19 Using Mobility Index: Evidence from Provinces in Java and Bali," Working Papers DP-2021-18, Economic Research Institute for ASEAN and East Asia (ERIA).
    14. De Backer, Bruno & Dewachter, Hans & Iania, Leonardo, 2021. "Macrofinancial information on the post-COVID-19 economic recovery: Will it be V, U or L-shaped?," Finance Research Letters, Elsevier, vol. 43(C).
    15. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2024. "Nowcasting Norwegian household consumption with debit card transaction data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1220-1244, November.
    16. Jakub Rybacki & Michał Gniazdowski, 2023. "Macroeconomic forecasting in Poland: lessons from the external shocks," Bank i Kredyt, Narodowy Bank Polski, vol. 54(1), pages 45-64.
    17. Orkideh Gharehgozli & Sunhyung Lee, 2022. "Money Supply and Inflation after COVID-19," Economies, MDPI, vol. 10(5), pages 1-14, April.
    18. James Chapman & Ajit Desai, 2022. "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers 22-10, Bank of Canada.
    19. Arbolino, Roberta & Caro, Paolo Di, 2021. "Can the EU funds promote regional resilience at time of Covid-19? Insights from the Great Recession11We thank the Editors and the four anonymous referees for helpful comments. We also thank Emanuele C," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 109-126.
    20. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    21. Severin Reissl & Alessandro Caiani & Francesco Lamperti & Mattia Guerini & Fabio Vanni & Giorgio Fagiolo & Tommaso Ferraresi & Leonardo Ghezzi & Mauro Napoletano & Andrea Roventini, 2021. "Assessing the economic effects of lockdowns in Italy: a computational Input-Output approach," LEM Papers Series 2021/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    22. Bas Scheer, 2022. "Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle," CPB Discussion Paper 434, CPB Netherlands Bureau for Economic Policy Analysis.
    23. Monica Laura Zlati & Costinela Fortea & Alina Meca & Valentin Marian Antohi, 2024. "Approaches to Prognosing the European Economic Crisis Through a New Economic–Financial Risk Sensitivity Model," Economies, MDPI, vol. 13(1), pages 1-30, December.
    24. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
    25. Frank Schorfheide & Dongho Song, 2020. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," Working Papers 20-26, Federal Reserve Bank of Philadelphia.
    26. Kevin Moran & Dalibor Stevanovic & Stephane Surprenant, 2024. "Risk Scenarios and Macroeconomic Forecasts," Working Papers 24-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised May 2025.
    27. Kevin Moran & Dalibor Stevanovic & Adam Abdel Kader Touré, 2020. "Macroeconomic Uncertainty and the COVID-19 Pandemic: Measure and Impacts on the Canadian Economy," CIRANO Working Papers 2020s-47, CIRANO.
    28. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
    29. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    30. Kevin Moran & Dalibor Stevanovic & Stéphane Surprenant, 2024. "Risk Scenarios and Macroeconomic Impacts: Insights for Canadian Policy," CIRANO Working Papers 2024s-03, CIRANO.
    31. İsmail Cakmak & Selcen Öztürk, 2023. "Analysing Impact of Economic Crises on Sector Profits with a New Approach," Prague Economic Papers, Prague University of Economics and Business, vol. 2023(3), pages 225-245.
    32. Shafiullah Qureshi & Ba Chu & Fanny S. Demers, 2021. "Forecasting Canadian GDP Growth with Machine Learning," Carleton Economic Papers 21-05, Carleton University, Department of Economics.
    33. John O’Trakoun, 2022. "Business forecasting during the pandemic," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 57(3), pages 95-110, July.
    34. Yannis Psycharis & Anastasia Panori & Dimitrios Athanasopoulos, 2022. "Public Investment and Regional Resilience: Empirical Evidence from the Greek Regions," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 113(1), pages 57-79, February.
    35. Archanskaia, Elizaveta & Canton, Erik & Hobza, Alexandr & Nikolov, Plamen & Simons, Wouter, 2023. "The asymmetric impact of COVID-19: A novel approach to quantifying financial distress across industries," European Economic Review, Elsevier, vol. 158(C).
    36. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
    37. Cassetti, Gabriele & Boitier, Baptiste & Elia, Alessia & Le Mouël, Pierre & Gargiulo, Maurizio & Zagamé, Paul & Nikas, Alexandros & Koasidis, Konstantinos & Doukas, Haris & Chiodi, Alessandro, 2023. "The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: An energy-economic modelling perspective," Energy, Elsevier, vol. 263(PC).
    38. Zhao, Xinyue & Chen, Heng & Zheng, Qiwei & Liu, Jun & Pan, Peiyuan & Xu, Gang & Zhao, Qinxin & Jiang, Xue, 2023. "Thermo-economic analysis of a novel hydrogen production system using medical waste and biogas with zero carbon emission," Energy, Elsevier, vol. 265(C).
    39. Aminullah, Erman, 2024. "Forecasting of technology innovation and economic growth in Indonesia," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    40. Antonio Oliva & Francesco Gracceva & Daniele Lerede & Matteo Nicoli & Laura Savoldi, 2021. "Projection of Post-Pandemic Italian Industrial Production through Vector AutoRegressive Models," Energies, MDPI, vol. 14(17), pages 1-18, September.
    41. Wang, Yuting & Chen, Heng & Qiao, Shichao & Pan, Peiyuan & Xu, Gang & Dong, Yuehong & Jiang, Xue, 2023. "A novel methanol-electricity cogeneration system based on the integration of water electrolysis and plasma waste gasification," Energy, Elsevier, vol. 267(C).
    42. Fabrizio Iacone & Luca Rossini & Andrea Viselli, 2024. "Comparing predictive ability in presence of instability over a very short time," Papers 2405.11954, arXiv.org.
    43. Nugroho, Anggoro Dimas Pambudi, 2022. "Strategi Ekonomi Bisnis dalam Upaya Menghadapi Ancaman Resesi 2023," OSF Preprints j3dpm, Center for Open Science.
    44. Fezzi, Carlo & Fanghella, Valeria, 2021. "Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe," European Economic Review, Elsevier, vol. 139(C).
    45. Lu, Fei & Zeng, Qing & Bouri, Elie & Tao, Ying, 2024. "Forecasting US GDP growth rates in a rich environment of macroeconomic data," International Review of Economics & Finance, Elsevier, vol. 95(C).
    46. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    47. Nadiia Shapovalenko, 2021. "A BVAR Model for Forecasting Ukrainian Inflation," IHEID Working Papers 05-2021, Economics Section, The Graduate Institute of International Studies.

  9. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.

    Cited by:

    1. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
    2. Barbara Rossi, 2021. "Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them," Journal of Economic Literature, American Economic Association, vol. 59(4), pages 1135-1190, December.
    3. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    4. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Papers 2008.01714, arXiv.org, revised Mar 2021.
    5. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Papers 2103.01926, arXiv.org, revised Jul 2021.
      • Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Working Papers 21-02, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    6. Klein, Tony, 2021. "Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock," QBS Working Paper Series 2021/07, Queen's University Belfast, Queen's Business School.
    7. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    8. Emilio Colombo & Matteo Pelagatti, 2019. "Statistical Learning and Exchange Rate Forecasting," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis1901, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    9. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
    10. Duo Qin & Sophie van Huellen & Qing Chao Wang & Thanos Moraitis, 2022. "Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data," Econometrics, MDPI, vol. 10(2), pages 1-22, April.
    11. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.
    12. Massimiliano Marcellino & Dalibor Stevanovic, 2022. "The demand and supply of information about inflation," Working Papers 22-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2022.
    13. Robert-Paul Berben & Rajni Rasiawan & Jasper de Winter, 2025. "Forecasting Dutch inflation using machine learning methods," Working Papers 828, DNB.
    14. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
    15. Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
    16. Rachidi Kotchoni & Dalibor Stevanovic, 2020. "GDP Forecast Accuracy During Recessions," Working Papers 20-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    17. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    18. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    19. Kozyrev, Boris, 2024. "Forecast combination and interpretability using random subspace," IWH Discussion Papers 21/2024, Halle Institute for Economic Research (IWH).
    20. 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.
    21. 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.
    22. Engelke, Carola & Heinisch, Katja & Schult, Christoph, 2019. "How forecast accuracy depends on conditioning assumptions," IWH Discussion Papers 18/2019, Halle Institute for Economic Research (IWH).
    23. Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
    24. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
    25. Xu, Yingying & Dai, Yifan & Guo, Lingling & Chen, Jingjing, 2024. "Leveraging machine learning to forecast carbon returns: Factors from energy markets," Applied Energy, Elsevier, vol. 357(C).
    26. Klein, Tony, 2022. "Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shock," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 264-286.
    27. Dias, Ishanka K. & Fernando, J.M. Ruwani & Fernando, P. Narada D., 2022. "Does investor sentiment predict bitcoin return and volatility? A quantile regression approach," International Review of Financial Analysis, Elsevier, vol. 84(C).
    28. 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.
    29. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.

  10. JACQUES Philippe, & LEROUX Marie-Louise, & STEVANOVIC Dalibor,, 2018. "Poverty among the elderly: The role of public pension systems," LIDAM Discussion Papers CORE 2018022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Yoko Niimi & Charles Yuji Horioka, 2023. "Elderly poverty and its measurement," Chapters, in: Jacques Silber (ed.), Research Handbook on Measuring Poverty and Deprivation, chapter 29, pages 307-315, Edward Elgar Publishing.
    2. Patricia Peinado & Felipe Serrano, 2024. "Minimum Pensions and Regional Income Redistribution in Spain," Hacienda Pública Española / Review of Public Economics, IEF, vol. 251(4), pages 51-79, December.
    3. Rajko Tomaš, 2022. "Measurement of the Concentration of Potential Quality of Life in Local Communities," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 163(1), pages 79-109, August.
    4. Koen Caminada & Kees Goudswaard & Jingqi Liu & Chen Wang & Jinxian Wang, 2025. "Decomposition of Changes in Elderly Poverty across 16 European Countries: 2005-2022," LIS Working papers 882, LIS Cross-National Data Center in Luxembourg.
    5. Abdul Hadi & Yogi Vidyattama & Badriah Badriah & Prihoda Emese, 2024. "Adequacy of the Pension System: A Qualitative Interview of Indonesian Civil Service Pensioners in Kapuas Regency," Economies, MDPI, vol. 12(12), pages 1-18, November.

  11. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Working Paper Series 2206, European Central Bank.

    Cited by:

    1. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    2. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    3. Sebastian Ankargren & Paulina Jon'eus, 2019. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Papers 1907.01075, arXiv.org.
    4. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.

  12. Dalibor Stevanovic & Rachidi Kotchoni & Maxime Leroux, 2017. "Forecasting economic activity in data-rich environment," CIRANO Working Papers 2017s-05, CIRANO.

    Cited by:

    1. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    2. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Working Paper Series 2206, European Central Bank.
    3. 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.
    4. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.

  13. Dalibor Stevanovic & Rachidi Kotchoni, 2016. "Forecasting U.S. Recessions and Economic Activity," CIRANO Working Papers 2016s-36, CIRANO.

    Cited by:

    1. Michael Dotsey & Shigeru Fujita & Tom Stark, 2018. "Do Phillips Curves Conditionally Help to Forecast Inflation?," International Journal of Central Banking, International Journal of Central Banking, vol. 14(4), pages 43-92, September.
    2. Manuel Paquette-Dupuis & Dalibor Stevanovic & Rachidi Kotchoni, 2019. "Prévisions de l’activité économique en temps de crise," CIRANO Project Reports 2019rp-04, CIRANO.

  14. Dalibor Stevanovic, 2015. "Factor augmented autoregressive distributed lag models with macroeconomic applications," CIRANO Working Papers 2015s-33, CIRANO.

    Cited by:

    1. Paul Beaudry & Franck Portier, 2014. "News Driven Business Cycles: Insights and Challenges," 2014 Meeting Papers 289, Society for Economic Dynamics.
    2. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.
    3. Jean Boivin & Marc P. Giannoni & Dalibor Stevanovic, 2016. "Dynamic Effects of Credit Shocks in a Data-Rich Environment," CIRANO Working Papers 2016s-55, CIRANO.
    4. Jean-Stéphane Mésonnier & Dalibor Stevanovic, 2012. "Bank Leverage Shocks And The Macroeconomy: A New Look In A Data-Rich Environment," CIRANO Papers 2012n-10a, CIRANO.
    5. Barattieri, Alessandro & Eden, Maya & Stevanovi, Dalibor, 2013. "The connection between Wall Street and Main Street : measurement and implications for monetary policy," Policy Research Working Paper Series 6667, The World Bank.

  15. Alessandro Barattieri & Maya Eden & Dalibor Stevanovic, 2015. "Financial Sector Interconnectedness and Monetary Policy Transmission," Carlo Alberto Notebooks 436, Collegio Carlo Alberto.

    Cited by:

    1. Daniel Carvalho, 2022. "Intra-financial assets and the intermediation role of the financial sector," Trinity Economics Papers tep0622, Trinity College Dublin, Department of Economics.
    2. Vincenzo Quadrini & Laura Moretti & Alessandro Barattieri, 2017. "Banks Interconnectivity and Leverage," 2017 Meeting Papers 504, Society for Economic Dynamics.
    3. Barattieri, Alessandro & Moretti, Laura & Quadrini, Vincenzo, 2021. "Banks funding, leverage, and investment," Journal of Financial Economics, Elsevier, vol. 141(1), pages 148-171.
    4. Saibal Ghosh, 2022. "Does financial interconnectedness affect monetary transmission? Evidence from India," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 15(3), pages 273-300, September.

  16. Dalibor Stevanovic & Charles Olivier Mao Takongmo, 2014. "Selection of the number of factors in presence of structural instability: a Monte Carlo study," CIRANO Working Papers 2014s-44, CIRANO.

    Cited by:

    1. 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.
    2. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.
    3. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.
    4. Mao Takongmo, Charles-O. & Touré, Adam, 2023. "Trade openness and connectedness of national productions: Do financial openness, economic specialization, and the size of the country matter?," Economic Modelling, Elsevier, vol. 125(C).
    5. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
    6. Dalibor Stevanovic & Rachidi Kotchoni & Maxime Leroux, 2017. "Forecasting economic activity in data-rich environment," CIRANO Working Papers 2017s-05, CIRANO.
    7. Charles Olivier Mao Takongmo, 2021. "DSGE models, detrending, and the method of moments," Bulletin of Economic Research, Wiley Blackwell, vol. 73(1), pages 67-99, January.

  17. Jean Boivin & Marc P. Giannoni & Dalibor Stevanovic, 2013. "Dynamic Effects of Credit Shocks in a Data-Rich Environment," CIRANO Working Papers 2013s-11, CIRANO.

    Cited by:

    1. Stevanovic Dalibor, 2016. "Common time variation of parameters in reduced-form macroeconomic models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 159-183, April.
    2. Nathan Bedock & Dalibor Stevanović, 2017. "An empirical study of credit shock transmission in a small open economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(2), pages 541-570, May.
    3. Francisco Covas & Ben Rump & Egon Zakrajšek, 2013. "Stress-testing U.S. bank holding companies: a dynamic panel quantile regression approach," Finance and Economics Discussion Series 2013-55, Board of Governors of the Federal Reserve System (U.S.).
    4. Dario Caldara & Cristina Fuentes-Albero & Simon Gilchrist & Egon Zakrajšek, 2016. "The Macroeconomic Impact of Financial and Uncertainty Shocks," International Finance Discussion Papers 1166, Board of Governors of the Federal Reserve System (U.S.).
    5. Fonseca, Marcelo Gonçalves da Silva & Pereira, Pedro L. Valls, 2014. "Credit shocks and monetary policy in Brazil: a structural FAVAR approach," Textos para discussão 358, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    6. Etoundi Atenga, Eric Martial & Abdo, Maman Hassan & Mougoué, Mbodja, 2021. "Financial Frictions and Macroeconomy During Financial Crises: A Bayesian DSGE Assessment," American Business Review, Pompea College of Business, University of New Haven, vol. 24(2), pages 62-99, November.
    7. Dalibor Stevanovic & Charles Olivier Mao Takongmo, 2014. "Selection of the number of factors in presence of structural instability: a Monte Carlo study," CIRANO Working Papers 2014s-44, CIRANO.
    8. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    9. Simon Beyeler & Sylvia Kaufmann, 2016. "Factor augmented VAR revisited - A sparse dynamic factor model approach," Working Papers 16.08, Swiss National Bank, Study Center Gerzensee.
    10. Régis Barnichon & Christian Matthes & Alexander Ziegenbein, 2016. "Theory Ahead of Measurement? Assessing the Nonlinear Effects of Financial Market Disruptions," Working Paper 16-15, Federal Reserve Bank of Richmond.
    11. Popp, Aaron & Zhang, Fang, 2016. "The macroeconomic effects of uncertainty shocks: The role of the financial channel," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 319-349.
    12. Alain Guay & Dalibor Stevanovic, 2025. "Estimation of Non-Gaussian SVAR Using Tensor Singular Value Decomposition," Working Papers 25-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Feb 2025.
    13. Jean-Stéphane Mésonnier & Dalibor Stevanovic, 2012. "Bank Leverage Shocks And The Macroeconomy: A New Look In A Data-Rich Environment," CIRANO Papers 2012n-10a, CIRANO.
    14. Banerjee, Ryan & Devereux, Michael B. & Lombardo, Giovanni, 2016. "Self-oriented monetary policy, global financial markets and excess volatility of international capital flows," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 275-297.
    15. Yohei Yamamoto, 2012. "Bootstrap Inference for Impulse Response Functions in Factor-Augmented Vector Autoregressions," Global COE Hi-Stat Discussion Paper Series gd12-249, Institute of Economic Research, Hitotsubashi University.
    16. Bofinger, Peter & Geißendörfer, Lisa & Haas, Thomas & Mayer, Fabian, 2021. "Discovering the True Schumpeter - New Insights into the Finance and Growth Nexus," CEPR Discussion Papers 16851, C.E.P.R. Discussion Papers.
    17. Mario Forni & Luca Gambetti & Nicolò Maffei-Faccioli & Luca Sala, 2022. "Nonlinear transmission of financial shocks: Some new evidence," Working Paper 2022/3, Norges Bank.
    18. Barnichon, Regis & Matthes, Christian & Ziegenbein, Alexander, 2016. "Assessing the Non-Linear Effects of Credit Market Shocks," CEPR Discussion Papers 11410, C.E.P.R. Discussion Papers.
    19. Simon Gilchrist & Egon Zakrajšek, 2019. "Trade Exposure and the Evolution of Inflation Dynamics," Finance and Economics Discussion Series 2019-007, Board of Governors of the Federal Reserve System (U.S.).
    20. Ulrichs Magdalena, 2018. "Identification of Financial and Macroeconomic Shocks in a Var Model of the Polish Economy. A Stability Analysis," Economics and Business Review, Sciendo, vol. 4(1), pages 29-43, April.
    21. Jean-Stéphane Mésonnier & Dalibor Stevanovic, 2017. "The Macroeconomic Effects of Shocks to Large Banks’ Capital," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 546-569, August.
    22. Pinter, Gabor & Theodoridis, Konstantinos & Yates, Tony, 2013. "Risk news shocks and the business cycle," Bank of England working papers 483, Bank of England.
    23. Antonio M. Conti & Andrea Nobili & Federico M. Signoretti, 2025. "Bank Capital Requirements, Lending Supply, and Economic Activity: A Scenario Analysis Perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(3), pages 1132-1164, April.
    24. Antonio M. Conti & Andrea Nobili & Federico M. Signoretti, 2018. "Bank capital constraints, lending supply and economic activity," Temi di discussione (Economic working papers) 1199, Bank of Italy, Economic Research and International Relations Area.
    25. Simon Beyeler & Sylvia Kaufmann, 2021. "Reduced‐form factor augmented VAR—Exploiting sparsity to include meaningful factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 989-1012, November.
    26. Dalibor Stevanovic & Rachidi Kotchoni & Maxime Leroux, 2017. "Forecasting economic activity in data-rich environment," CIRANO Working Papers 2017s-05, CIRANO.
    27. Österholm, Pär, 2018. "The relation between treasury yields and corporate bond yield spreads in Australia: Evidence from VARs," Finance Research Letters, Elsevier, vol. 24(C), pages 186-192.
    28. Tihana Skrinjaric, 2022. "Macroeconomic effects of systemic stress: a rolling spillover index approach," Public Sector Economics, Institute of Public Finance, vol. 46(1), pages 109-140.
    29. Anastasios Evgenidis & Dionisis Philippas & Costas Siriopoulos, 2019. "Heterogeneous effects in the international transmission of the US monetary policy: a factor-augmented VAR perspective," Empirical Economics, Springer, vol. 56(5), pages 1549-1579, May.
    30. Marzie Taheri Sanjani, 2014. "Financial Frictions in Data: Evidence and Impact," IMF Working Papers 2014/238, International Monetary Fund.

  18. Alessandro Barattieri & Maya Eden & Dalibor Stevanovic, 2013. "The Connection between Wall Street and Main Street: Measurement and Implications for Monetary Policy," CIRANO Working Papers 2013s-31, CIRANO.

    Cited by:

    1. Erhan Uluceviz & Kamil Yilmaz, 2018. "Measuring Real-Financial Connectedness in the U.S. Economy," Koç University-TUSIAD Economic Research Forum Working Papers 1812, Koc University-TUSIAD Economic Research Forum.

  19. Nathan Bedock & Dalibor Stevanovic, 2012. "An Empirical Study of Credit Shock Transmission in a Small Open Economy," CIRANO Working Papers 2012s-16, CIRANO.

    Cited by:

    1. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.
    2. Chinara Azizova & Bruno Feunou & James Kyeong, 2023. "Forecasting Risks to the Canadian Economic Outlook at a Daily Frequency," Discussion Papers 2023-19, Bank of Canada.
    3. Kevin Moran & Dalibor Stevanovic & Stephane Surprenant, 2024. "Risk Scenarios and Macroeconomic Forecasts," Working Papers 24-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised May 2025.
    4. Kevin Moran & Dalibor Stevanovic & Adam Abdel Kader Touré, 2020. "Macroeconomic Uncertainty and the COVID-19 Pandemic: Measure and Impacts on the Canadian Economy," CIRANO Working Papers 2020s-47, CIRANO.
    5. Maxime Leboeuf & Daniel Hyun, 2018. "Is the Excess Bond Premium a Leading Indicator of Canadian Economic Activity?," Staff Analytical Notes 2018-4, Bank of Canada.
    6. Kevin Moran & Dalibor Stevanovic & Stéphane Surprenant, 2024. "Risk Scenarios and Macroeconomic Impacts: Insights for Canadian Policy," CIRANO Working Papers 2024s-03, CIRANO.
    7. Dalibor Stevanovic & Rachidi Kotchoni & Maxime Leroux, 2017. "Forecasting economic activity in data-rich environment," CIRANO Working Papers 2017s-05, CIRANO.

  20. Jean-Stéphane Mésonnier & Stevanovic, D., 2012. "Bank leverage shocks and the macroeconomy: a new look in a data-rich environment," Working papers 394, Banque de France.

    Cited by:

    1. Kok, Christoffer & Gross, Marco & Żochowski, Dawid, 2016. "The impact of bank capital on economic activity - evidence from a mixed-cross-section GVAR model," Working Paper Series 1888, European Central Bank.
    2. Jean Barthélemy & Magali Marx, 2012. "Generalizing the Taylor Principle: New Comment," Working Papers hal-03461113, HAL.
    3. Peltonen, Tuomas A. & Gross, Marco & Behn, Markus, 2016. "Assessing the costs and benefits of capital-based macroprudential policy," Working Paper Series 1935, European Central Bank.
    4. Simona Malovana & Martin Hodula & Josef Bajzik & Zuzana Gric, 2021. "A Tale of Different Capital Ratios: How to Correctly Assess the Impact of Capital Regulation on Lending," Working Papers 2021/8, Czech National Bank, Research and Statistics Department.
    5. Barattieri, Alessandro & Eden, Maya & Stevanovi, Dalibor, 2013. "The connection between Wall Street and Main Street : measurement and implications for monetary policy," Policy Research Working Paper Series 6667, The World Bank.

Articles

  1. 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.
    See citations under working paper version above.
  2. Kevin Moran & Dalibor Stevanovic & Adam Kader Touré, 2022. "Macroeconomic uncertainty and the COVID‐19 pandemic: Measure and impacts on the Canadian economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 379-405, February.
    See citations under working paper version above.
  3. 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.
    See citations under working paper version above.
  4. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    See citations under working paper version above.
  5. Philippe Jacques & Marie-Louise Leroux & Dalibor Stevanovic, 2021. "Poverty among the elderly: the role of public pension systems," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 28(1), pages 24-67, February.
    See citations under working paper version above.
  6. Goulet Coulombe, Philippe & Marcellino, Massimiliano & Stevanović, Dalibor, 2021. "Can Machine Learning Catch The Covid-19 Recession?," National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 71-109, May.
    See citations under working paper version above.
  7. 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.
    See citations under working paper version above.
  8. Barattieri, Alessandro & Eden, Maya & Stevanovic, Dalibor, 2020. "Risk sharing, efficiency of capital allocation, and the connection between banks and the real economy," Journal of Corporate Finance, Elsevier, vol. 60(C).

    Cited by:

    1. Sabri Boubaker & T.D.Q. Le & T. Ngo, 2023. "Managing Bank Performance under COVID-19: A Novel Inverse DEA Efficiency Approach," Post-Print hal-04435441, HAL.
    2. Elnahass, Marwa & Trinh, Vu Quang & Li, Teng, 2021. "Global banking stability in the shadow of Covid-19 outbreak," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    3. Zhang, Shangfeng & Chen, Congcong & Xu, Siwa & Xu, Bing, 2021. "Measurement of capital allocation efficiency in emerging economies: evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    4. Ameni Ghenimi & Hasna Chaibi & Mohamed Ali Omri, 2024. "Risk and performance of Islamic and conventional banks under COVID-19 pandemic: Evidence from MENA region," Arab Gulf Journal of Scientific Research, Emerald Group Publishing Limited, vol. 42(4), pages 1788-1804, January.

  9. Jean Boivin & Marc P. Giannoni & Dalibor Stevanović, 2020. "Dynamic Effects of Credit Shocks in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 272-284, April.
    See citations under working paper version above.
  10. Barattieri, Alessandro & Eden, Maya & Stevanovic, Dalibor, 2019. "Financial Sector Interconnectedness And Monetary Policy Transmission," Macroeconomic Dynamics, Cambridge University Press, vol. 23(3), pages 1074-1101, April.
    See citations under working paper version above.
  11. 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.
    See citations under working paper version above.
  12. Claudia Foroni & Massimiliano Marcellino & Dalibor Stevanovic, 2019. "Mixed‐frequency models with moving‐average components," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 688-706, August.

    Cited by:

    1. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.
    2. Luca Fanelli & Antonio Marsi, 2021. "Unconventional Monetary Policy in the Euro Area: A Tale of Three Shocks," Working Papers wp1164, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Fanelli, Luca & Marsi, Antonio, 2022. "Sovereign spreads and unconventional monetary policy in the Euro area: A tale of three shocks," European Economic Review, Elsevier, vol. 150(C).

  13. Nathan Bedock & Dalibor Stevanovic, 2017. "An empirical study of credit shock transmission in a small open economy," Canadian Journal of Economics, Canadian Economics Association, vol. 50(2), pages 541-570, May.
    See citations under working paper version above.
  14. Jean-Stéphane Mésonnier & Dalibor Stevanovic, 2017. "The Macroeconomic Effects of Shocks to Large Banks’ Capital," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 546-569, August.

    Cited by:

    1. Tölö, Eero & Miettinen, Paavo, 2018. "How do shocks to bank capital affect lending and growth?," Bank of Finland Research Discussion Papers 25/2018, Bank of Finland.
    2. Kanngiesser Derrick & Martin Reiner & Maurin Laurent & Moccero Diego, 2020. "The macroeconomic impact of shocks to bank capital buffers in the Euro Area," The B.E. Journal of Macroeconomics, De Gruyter, vol. 20(1), pages 1-17, January.
    3. Couaillier, Cyril, 2021. "What are banks’ actual capital targets?," Working Paper Series 2618, European Central Bank.
    4. Barattieri, Alessandro & Eden, Maya & Stevanovic, Dalibor, 2020. "Risk sharing, efficiency of capital allocation, and the connection between banks and the real economy," Journal of Corporate Finance, Elsevier, vol. 60(C).
    5. Simona Malovaná & Dominika Ehrenbergerová, 2022. "The effect of higher capital requirements on bank lending: the capital surplus matters," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 49(3), pages 793-832, August.
    6. Liu, Guangling & Molise, Thabang, 2019. "Housing and credit market shocks: Exploring the role of rule-based Basel III counter-cyclical capital requirements," Economic Modelling, Elsevier, vol. 82(C), pages 264-279.
    7. Davidson, Sharada Nia & Moccero, Diego Nicolas, 2024. "The nonlinear effects of banks’ vulnerability to capital depletion in euro area countries," Working Paper Series 2912, European Central Bank.
    8. Antonio M. Conti & Andrea Nobili & Federico M. Signoretti, 2025. "Bank Capital Requirements, Lending Supply, and Economic Activity: A Scenario Analysis Perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(3), pages 1132-1164, April.
    9. Antonio M. Conti & Andrea Nobili & Federico M. Signoretti, 2018. "Bank capital constraints, lending supply and economic activity," Temi di discussione (Economic working papers) 1199, Bank of Italy, Economic Research and International Relations Area.
    10. Simona Malovana & Martin Hodula & Josef Bajzik & Zuzana Gric, 2021. "A Tale of Different Capital Ratios: How to Correctly Assess the Impact of Capital Regulation on Lending," Working Papers 2021/8, Czech National Bank, Research and Statistics Department.
    11. Huljak, Ivan & Martin, Reiner & Moccero, Diego & Pancaro, Cosimo, 2020. "Do non-performing loans matter for bank lending and the business cycle in euro area countries?," Working Paper Series 2411, European Central Bank.
    12. Guangling Liu & Thabang Molise, 2018. "Is Basel III counter-cyclical: The case of South Africa?," Working Papers 10/2018, Stellenbosch University, Department of Economics.
    13. Wang, Ling, 2023. "Central bank asset purchases, banks’ risky security holdings and profitability: Macro and micro evidence from Japan and the U.S," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 347-364.
    14. Conti, Antonio M. & Nobili, Andrea & Signoretti, Federico M., 2023. "Bank capital requirement shocks: A narrative perspective," European Economic Review, Elsevier, vol. 151(C).

  15. Stevanovic Dalibor, 2016. "Common time variation of parameters in reduced-form macroeconomic models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 159-183, April.

    Cited by:

    1. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Bayesian Modeling of Time-Varying Parameters Using Regression Trees," Working Papers 23-05, Federal Reserve Bank of Cleveland.
    2. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    3. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2022. "Bayesian Modeling of TVP-VARs Using Regression Trees," Papers 2209.11970, arXiv.org, revised May 2023.
    4. Simon Beyeler, 2019. "Streamlining Time-varying VAR with a Factor Structure in the Parameters," Working Papers 19.03, Swiss National Bank, Study Center Gerzensee.
    5. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
    6. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    7. Emilian DOBRESCU, 2017. "Modelling an Emergent Economy and Parameter Instability Problem," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-28, June.
    8. Dalibor Stevanovic & Rachidi Kotchoni & Maxime Leroux, 2017. "Forecasting economic activity in data-rich environment," CIRANO Working Papers 2017s-05, CIRANO.
    9. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.

  16. Mao Takongmo, Charles Olivier & Stevanovic, Dalibor, 2015. "Selection Of The Number Of Factors In Presence Of Structural Instability: A Monte Carlo Study," L'Actualité Economique, Société Canadienne de Science Economique, vol. 91(1-2), pages 177-233, Mars-Juin.
    See citations under working paper version above.
  17. Jean-Marie Dufour & Dalibor Stevanović, 2013. "Factor-Augmented VARMA Models With Macroeconomic Applications," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 491-506, October.

    Cited by:

    1. Nathan Bedock & Dalibor Stevanović, 2017. "An empirical study of credit shock transmission in a small open economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(2), pages 541-570, May.
    2. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.
    3. Dalibor Stevanovic & Charles Olivier Mao Takongmo, 2014. "Selection of the number of factors in presence of structural instability: a Monte Carlo study," CIRANO Working Papers 2014s-44, CIRANO.
    4. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    5. Monika Bours & Ansgar Steland, 2021. "Large‐sample approximations and change testing for high‐dimensional covariance matrices of multivariate linear time series and factor models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 610-654, June.
    6. Gustavo Fruet Dias & George Kapetanios, 2014. "Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets," CREATES Research Papers 2014-37, Department of Economics and Business Economics, Aarhus University.
    7. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    8. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Working Paper Series 2206, European Central Bank.
    10. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
    11. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
    12. 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.
    13. Gil-Alana, Luis A. & Gupta, Rangan & Olubusoye, Olusanya E. & Yaya, OlaOluwa S., 2016. "Time series analysis of persistence in crude oil price volatility across bull and bear regimes," Energy, Elsevier, vol. 109(C), pages 29-37.
    14. Dalibor Stevanovic & Rachidi Kotchoni & Maxime Leroux, 2017. "Forecasting economic activity in data-rich environment," CIRANO Working Papers 2017s-05, CIRANO.
    15. Dalibor Stevanovic, 2015. "Factor augmented autoregressive distributed lag models with macroeconomic applications," CIRANO Working Papers 2015s-33, CIRANO.
    16. Zongwu Cai & Xiyuan Liu, 2021. "Solving the Price Puzzle Via A Functional Coefficient Factor-Augmented VAR Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202106, University of Kansas, Department of Economics, revised Jan 2021.
    17. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    18. Antoine A. Djogbenou, 2024. "Identifying oil price shocks with global, developed, and emerging latent real economy activity factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 128-149, January.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.