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Identifying business cycle turning points in real time with vector quantization

Citations

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Cited by:

  1. Jiayan YU & Jingqian ZHANG & Hee Eun SHIN & Jooan KONG, 2019. "Revisiting the Economic Crisis after a Decade: Statistical and Machine Learning Perspectives," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 14-19.
  2. 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.
  3. Kovacs Kevin & Boulier Bryan & Stekler Herman, 2017. "Nowcasting: Identifying German Cyclical Turning Points," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(4), pages 329-341, August.
  4. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2024. "Downside risk reduction using regime-switching signals: a statistical jump model approach," Journal of Asset Management, Palgrave Macmillan, vol. 25(5), pages 493-507, September.
  5. Keijsers, Bart & van Dijk, Dick, 2025. "Does economic uncertainty predict real activity in real time?," International Journal of Forecasting, Elsevier, vol. 41(2), pages 748-762.
  6. Li, Haixi & Sheng, Xuguang Simon & Yang, Jingyun, 2021. "Monitoring recessions: A Bayesian sequential quickest detection method," International Journal of Forecasting, Elsevier, vol. 37(2), pages 500-510.
  7. Qingyuan Han, 2025. "Equity Market Price Changes Are Predictable: A Natural Science Approach," Papers 2510.01542, arXiv.org.
  8. Azqueta-Gavaldon, Andres & Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2020. "Nowcasting business cycle turning points with stock networks and machine learning," Working Paper Series 2494, European Central Bank.
  9. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
  10. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
  11. Joshua C. C. Chan & Michael Pfarrhofer, 2025. "Large Bayesian VARs for Binary and Censored Variables," Papers 2506.01422, arXiv.org.
  12. Pawel Dlotko & Simon Rudkin, 2019. "The Topology of Time Series: Improving Recession Forecasting from Yield Spreads," Working Papers 2019-02, Swansea University, School of Management.
  13. 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.
  14. Juhro, Solikin M. & Iyke, Bernard Njindan & Narayan, Paresh Kumar, 2024. "Capital flow dynamics and the synchronization of financial cycles and business cycles in emerging market economies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
  15. Tara M. Sinclair, 2019. "Continuities and Discontinuities in Economic Forecasting," Working Papers 2019-003, The George Washington University, The Center for Economic Research.
  16. Troy Davig & Aaron Smalter Hall, 2016. "Recession forecasting using Bayesian classification," Research Working Paper RWP 16-6, Federal Reserve Bank of Kansas City.
  17. James Morley, 2018. "The Econometric Analysis of Recurrent Events in Macroeconomics and Finance," The Economic Record, The Economic Society of Australia, vol. 94(306), pages 338-340, September.
  18. He, Yongda & Lin, Boqiang, 2019. "Regime differences and industry heterogeneity of the volatility transmission from the energy price to the PPI," Energy, Elsevier, vol. 176(C), pages 900-916.
  19. Ming-Chu Chiang & Tien Foo Sing & Long Wang, 2020. "Interactions Between Housing Market and Stock Market in the United States: A Markov Switching Approach," Journal of Real Estate Research, Taylor & Francis Journals, vol. 42(4), pages 552-571, October.
  20. Pierdzioch Christian & Gupta Rangan, 2020. "Uncertainty and Forecasts of U.S. Recessions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-20, September.
  21. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
  22. Huang, Yu-Fan & Startz, Richard, 2020. "Improved recession dating using stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 507-514.
  23. Michael W. McCracken & Joseph T. McGillicuddy & Michael T. Owyang, 2022. "Binary Conditional Forecasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1246-1258, June.
  24. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
  25. Soybilgen, Baris, 2018. "Identifying US business cycle regimes using dynamic factors and neural network models," MPRA Paper 94715, University Library of Munich, Germany.
  26. de Bondt, Gabe J. & Hahn, Elke & Zekaite, Zivile, 2021. "ALICE: Composite leading indicators for euro area inflation cycles," International Journal of Forecasting, Elsevier, vol. 37(2), pages 687-707.
  27. Pascal Michaillat, 2025. "Recession Detection Using Classifiers on the Anticipation-Precision Frontier," Papers 2506.09664, arXiv.org, revised Dec 2025.
  28. Yongchen Zhao, 2020. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 77-97, November.
  29. Marco Hoeberichts & Jan Willem van den End, 2024. "Detecting turning points in the inflation cycle," Working Papers 808, DNB.
  30. Ines Fortin & Sebastian P. Koch & Klaus Weyerstrass, 2020. "Evaluation of economic forecasts for Austria," Empirical Economics, Springer, vol. 58(1), pages 107-137, January.
  31. Maximo Camacho & María Dolores Gadea & Ana Gómez Loscos, 2022. "A New Approach to Dating the Reference Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 66-81, January.
  32. Lampe, Max & Adalid, Ramón, 2025. "A machine learning approach to real time identification of turning points in monetary aggregates M1 and M3," Working Paper Series 3148, European Central Bank.
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