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Signaling NBER turning points: a sequential approach

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  • Vasyl Golosnoy
  • Jens Hogrefe

Abstract

The dates of the U.S. business cycle are reported by the National Bureau of Economic Research with a considerable delay, so an early notion of turning points is of particular interest. This paper proposes a novel sequential classification approach designed for timely signaling these turning points, using the time series of coincident economic indicators. The approach exhibits a range of theoretical optimality properties for early signaling, moreover, it is transparent and easy to implement. The empirical study evaluates the signaling ability of the proposed methodology.

Suggested Citation

  • Vasyl Golosnoy & Jens Hogrefe, 2013. "Signaling NBER turning points: a sequential approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 438-448, February.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:438-448
    DOI: 10.1080/02664763.2012.748017
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    References listed on IDEAS

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    1. David Bock, 2008. "Aspects on the control of false alarms in statistical surveillance and the impact on the return of financial decision systems," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(2), pages 213-227.
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    4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    5. Vasyl Golosnoy & Sergiy Ragulin & Wolfgang Schmid, 2009. "Multivariate CUSUM chart: properties and enhancements," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 93(3), pages 263-279, September.
    6. Harald Uhlig, 1997. "Bayesian Vector Autoregressions with Stochastic Volatility," Econometrica, Econometric Society, vol. 65(1), pages 59-74, January.
    7. E. Andersson & D. Bock & M. Frisen, 2006. "Some statistical aspects of methods for detection of turning points in business cycles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(3), pages 257-278.
    8. Kontolemis, Zenon G, 2001. "Analysis of the US Business Cycle with a Vector-Markov-Switching Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 47-61, January.
    9. Golosnoy, Vasyl & Hogrefe, Jens, 2009. "Sequential methodology for signaling business cycle turning points," Kiel Working Papers 1528, Kiel Institute for the World Economy (IfW Kiel).
    10. David Bock & Eva Andersson & Marianne Frisén, 2005. "Statistical surveillance of cyclical processes with application to turns in business cycles," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(7), pages 465-490.
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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. Camillo Cammarota, 2017. "Estimating the turning point location in shifted exponential model of time series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(7), pages 1269-1281, May.
    3. Vasyl Golosnoy & Anja Rossen, 2018. "Modeling dynamics of metal price series via state space approach with two common factors," Empirical Economics, Springer, vol. 54(4), pages 1477-1501, June.
    4. Jamol Bahromov, 2022. "Regime-switching empirical similarity model: a comparison with baseline models," Empirical Economics, Springer, vol. 63(5), pages 2655-2674, November.

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