Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach
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DOI: 10.1111/j.1540-5982.2011.01648.x
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- Zhihong Chen & Azhar Iqbal & Huiwen Lai, 2011. "Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach," Canadian Journal of Economics, Canadian Economics Association, vol. 44(2), pages 651-672, May.
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- Fornaro, Paolo, 2015. "Forecasting U.S. Recessions with a Large Set of Predictors," MPRA Paper 62973, University Library of Munich, Germany.
- Marius M. Mihai, 2020. "Do credit booms predict US recessions?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 887-910, September.
- Davig, Troy & Hall, Aaron Smalter, 2019. "Recession forecasting using Bayesian classification," International Journal of Forecasting, Elsevier, vol. 35(3), pages 848-867.
- 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.
- Massimo Ferrari Minesso & Laura Lebastard & Helena Mezo, 2023.
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- Le Mezo, Helena & Ferrari Minesso, Massimo, 2021. "Text-based recession probabilities," Working Paper Series 2516, European Central Bank.
- Harri Ponka, 2017.
"The Role of Credit in Predicting US Recessions,"
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- Harri Pönkä, 2015. "The Role of Credit in Predicting US Recessions," CREATES Research Papers 2015-48, Department of Economics and Business Economics, Aarhus University.
- 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.
- Heikki Kauppi, 2019. "Recession Prediction with OptimalUse of Leading Indicators," Discussion Papers 125, Aboa Centre for Economics.
- 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.
- 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.
- 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.
- Troy Davig & Aaron Smalter Hall, 2016. "Recession forecasting using Bayesian classification," Research Working Paper RWP 16-6, Federal Reserve Bank of Kansas City.
- 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.
- 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.
- Donato Ceci & Andrea Silvestrini, 2023.
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- Pönkä, Harri & Stenborg, Markku, 2018. "Forecasting the state of the Finnish business cycle," MPRA Paper 91226, University Library of Munich, Germany.
- 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.
- 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).
- Soybilgen, Baris, 2018. "Identifying US business cycle regimes using dynamic factors and neural network models," MPRA Paper 94715, University Library of Munich, Germany.
- 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.
- 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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More about this item
JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
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