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Estimating Turning Points Using Large Data Sets


  • James H. Stock
  • Mark W. Watson


Dating business cycles entails ascertaining economy-wide turning points. Broadly speaking, there are two approaches in the literature. The first approach, which dates to Burns and Mitchell (1946), is to identify turning points individually in a large number of series, then to look for a common date that could be called an aggregate turning point. The second approach, which has been the focus of more recent academic and applied work, is to look for turning points in a few, or just one, aggregate. This paper examines these two approaches to the identification of turning points. We provide a nonparametric definition of a turning point (an estimand) based on a population of time series. This leads to estimators of turning points, sampling distributions, and standard errors for turning points based on a sample of series. We consider both simple random sampling and stratified sampling. The empirical part of the analysis is based on a data set of 270 disaggregated monthly real economic time series for the U.S., 1959-2010.

Suggested Citation

  • James H. Stock & Mark W. Watson, 2010. "Estimating Turning Points Using Large Data Sets," NBER Working Papers 16532, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:16532
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    References listed on IDEAS

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    2. 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.
    3. Charles, Amélie & Darné, Olivier & Diebolt, Claude & Ferrara, Laurent, 2015. "A new monthly chronology of the US industrial cycles in the prewar economy," Journal of Financial Stability, Elsevier, vol. 17(C), pages 3-9.
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    5. 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.
    6. 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.
    7. Knut Are Aastveit & André K. Anundsen & Eyo I. Herstad, 2017. "Residential investment and recession predictability," Working Papers No 8/2017, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
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    9. Grigoraş, Veaceslav & Stanciu, Irina Eusignia, 2016. "New evidence on the (de)synchronisation of business cycles: Reshaping the European business cycle," International Economics, Elsevier, vol. 147(C), pages 27-52.
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    13. Canova, Fabio & Schlaepfer, Alan, 2014. "Has the Euro-Mediterranean partnership affected Mediterranean business cycles?," CEPR Discussion Papers 10023, C.E.P.R. Discussion Papers.
    14. 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.
    15. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Annals of Economics and Statistics, GENES, issue 123-124, pages 333-361.
    16. Catherine Doz & Anna Petronevich, 2015. "Dating Business Cycle Turning Points for the French Economy: a MS-DFM approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01159200, HAL.
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    18. Koijen, Ralph & van Binsbergen, Jules H., 2015. "The Term Structure of Returns: Facts and Theory," CEPR Discussion Papers 10633, C.E.P.R. Discussion Papers.
    19. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an uncertain economic environment," Working Paper 2014/17, Norges Bank.
    20. Huseyin Kaya, 2013. "On the Predictive Power of Yield Spread for Future Growth and Recession: The Turkish Case," Working Papers 010, Bahcesehir University, Betam, revised Mar 2013.
    21. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    22. Gabriele Galati & Irma Hindrayanto & Siem Jan Koopman & Marente Vlekke, 2016. "Measuring financial cycles with a model-based filter: Empirical evidence for the United States and the euro area," DNB Working Papers 495, Netherlands Central Bank, Research Department.
    23. Yasutomo Murasawa, 2016. "The Beveridge–Nelson decomposition of mixed-frequency series," Empirical Economics, Springer, vol. 51(4), pages 1415-1441, December.
    24. Sadullah Çelik & Deniz Şatıroğlu, 2015. "A Reality Check on the Relationship between Poverty and Income Inequality for Turkey," EY International Congress on Economics II (EYC2015), November 5-6, 2015, Ankara, Turkey 229, Ekonomik Yaklasim Association.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles


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