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Identifying business cycle turning points in real time with vector quantization

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  • Giusto, Andrea
  • Piger, Jeremy
Abstract
We propose a simple machine-learning algorithm known as Learning Vector Quantization (LVQ) for the purpose of identifying new U.S. business cycle turning points quickly in real time. LVQ is used widely for real-time statistical classification in many other fields, but has not previously been applied to the classification of economic variables, to the best of our knowledge. The algorithm is intuitive and simple to implement, and easily incorporates salient features of the real-time nowcasting environment, such as differences in data reporting lags across series. We evaluate the algorithm’s real-time ability to establish new business cycle turning points in the United States quickly and accurately over the past five NBER recessions. Despite its relative simplicity, the algorithm’s performance appears to be very competitive with those of commonly used alternatives.

Suggested Citation

  • Giusto, Andrea & Piger, Jeremy, 2017. "Identifying business cycle turning points in real time with vector quantization," International Journal of Forecasting, Elsevier, vol. 33(1), pages 174-184.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:174-184
    DOI: 10.1016/j.ijforecast.2016.04.006
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    References listed on IDEAS

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    Cited by:

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    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. 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.
    6. 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.
    7. 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.
    8. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    9. 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).
    10. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    11. 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.
    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. Tara M. Sinclair, 2019. "Continuities and Discontinuities in Economic Forecasting," Working Papers 2019-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    15. Troy Davig & Aaron Smalter Hall, 2016. "Recession forecasting using Bayesian classification," Research Working Paper RWP 16-6, Federal Reserve Bank of Kansas City.
    16. 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.
    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. 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.
    20. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
    21. Huang, Yu-Fan & Startz, Richard, 2020. "Improved recession dating using stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 507-514.
    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. 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.
    24. Marco Hoeberichts & Jan Willem van den End, 2024. "Detecting turning points in the inflation cycle," Working Papers 808, DNB.
    25. Ines Fortin & Sebastian P. Koch & Klaus Weyerstrass, 2020. "Evaluation of economic forecasts for Austria," Empirical Economics, Springer, vol. 58(1), pages 107-137, January.

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