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Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach

Citations

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Cited by:

  1. Barış Soybilgen, 2020. "Identifying US business cycle regimes using dynamic factors and neural network models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 827-840, August.
  2. Fornaro, Paolo, 2015. "Forecasting U.S. Recessions with a Large Set of Predictors," MPRA Paper 62973, University Library of Munich, Germany.
  3. Marius M. Mihai, 2020. "Do credit booms predict US recessions?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 887-910, September.
  4. Davig, Troy & Hall, Aaron Smalter, 2019. "Recession forecasting using Bayesian classification," International Journal of Forecasting, Elsevier, vol. 35(3), pages 848-867.
  5. Nissilä, Wilma, 2020. "Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland," BoF Economics Review 7/2020, Bank of Finland.
  6. Massimo Ferrari Minesso & Laura Lebastard & Helena Mezo, 2023. "Text-Based Recession Probabilities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 415-438, June.
  7. Rahul Billakanti & Minchul Shin, 2026. "At-Risk Transformation for U.S. Recession Prediction," Papers 2603.07813, arXiv.org.
  8. Kevin Moran & Simplice Aime Nono, 2016. "Using Confidence Data to Forecast the Canadian Business Cycle," Cahiers de recherche 1606, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
  9. Charles S. Gascon & Joseph Martorana, 2024. "The Beige Book and the Business Cycle: Using Beige Book Anecdotes to Construct Recession Probabilities," Working Papers 2024-037, Federal Reserve Bank of St. Louis, revised 06 Dec 2024.
  10. Heikki Kauppi, 2019. "Recession Prediction with OptimalUse of Leading Indicators," Discussion Papers 125, Aboa Centre for Economics.
  11. Nataša Erjavec & Petar Soriæ & Mirjana Èižmešija, 2016. "Predicting the probability of recession in Croatia: Is economic sentiment the missing link?," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 34(2), pages 555-579.
  12. Baris Soybilgen, 2017. "Identifying Us Business Cycle Regimes Using Factor Augmented Neural Network Models," Working Papers 1703, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
  13. 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.
  14. Troy Davig & Aaron Smalter Hall, 2016. "Recession forecasting using Bayesian classification," Research Working Paper RWP 16-6, Federal Reserve Bank of Kansas City.
  15. Yang Aijun & Xiang Ju & Yang Hongqiang & Lin Jinguan, 2018. "Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1123-1138, April.
  16. Huiwen Lai & Eric C. Y. Ng, 2020. "On business cycle forecasting," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-26, December.
  17. 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.
  18. Harri Pönkä & Markku Stenborg, 2020. "Forecasting the state of the Finnish business cycle," Finnish Economic Papers, Finnish Economic Association, vol. 29(1), pages 81-99, Spring.
  19. Bellégo, C. & Ferrara, L., 2012. "Macro-financial linkages and business cycles: A factor-augmented probit approach," Economic Modelling, Elsevier, vol. 29(5), pages 1793-1797.
  20. Alonso-Alvarez, Irma & Molina, Luis, 2023. "How to foresee crises? A new synthetic index of vulnerabilities for emerging economies," Economic Modelling, Elsevier, vol. 125(C).
  21. Harri Ponka, 2017. "The Role of Credit in Predicting US Recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 469-482, August.
  22. Soybilgen, Baris, 2018. "Identifying US business cycle regimes using dynamic factors and neural network models," MPRA Paper 94715, University Library of Munich, Germany.
  23. Pascal Michaillat, 2025. "Recession Detection Using Classifiers on the Anticipation-Precision Frontier," Papers 2506.09664, arXiv.org, revised Dec 2025.
  24. Proaño, Christian R. & Theobald, Thomas, 2014. "Predicting recessions with a composite real-time dynamic probit model," International Journal of Forecasting, Elsevier, vol. 30(4), pages 898-917.
  25. Philip Rademacher, 2025. "Forecasting Recessions in Germany with Feature Selection and Machine Learning," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 21(2), pages 119-157, December.
  26. Irma Alonso & Luis Molina, 2019. "The SHERLOC: an EWS-based index of vulnerability for emerging economies," Working Papers 1946, Banco de España.
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