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A chronology of international business cycles through non-parametric decoding

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This paper introduces a new empirical strategy for the characterization of business cycles. It combines non-parametric decoding methods that classify a series into expansions and recessions but does not require specification of the underlying stochastic process generating the data. It then uses network analysis to combine the signals obtained from different economic indicators to generate a unique chronology. These methods generate a record of peak and trough dates comparable, and in one sense superior, to the NBER's own chronology. The methods are then applied to 22 OECD countries to obtain a global business cycle chronology.

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  • Travis J. Berge & Shu-Chun Chen & Hsieh Fushing & Òscar Jordà, 2010. "A chronology of international business cycles through non-parametric decoding," Research Working Paper RWP 11-13, Federal Reserve Bank of Kansas City.
    • Handle: RePEc:fip:fedkrw:rwp11-13
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    Cited by:

    1. Sergey V. Smirnov & Nikolay V. Kondrashov & Anna V. Petronevich, 2017. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 53-73, May.
    2. Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
    3. Travis Berge & Òscar Jordà, 2013. "A chronology of turning points in economic activity: Spain, 1850–2011," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 4(1), pages 1-34, March.
    4. Andrea Giusto & Jeremy Piger, 2013. "Nowcasting U.S. Business Cycle Turning Points with Vector Quantization," Working Papers daleconwp2013-02, Dalhousie University, Department of Economics.
    5. Adrian Pagan, 2013. "Patterns and Their Uses," NCER Working Paper Series 96, National Centre for Econometric Research.
    6. 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.
    7. Sharma, Susan Sunila & Narayan, Paresh Kumar, 2022. "Technology shocks and stock returns: A long-term perspective," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 67-83.
    8. Kroencke, Tim A., 2022. "Recessions and the stock market," Journal of Monetary Economics, Elsevier, vol. 131(C), pages 61-77.

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • F42 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - International Policy Coordination and Transmission

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