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Calling recessions in real time

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  • Hamilton, James D.

Abstract

This paper surveys efforts to automate the dating of business cycle turning points. Doing this on a real time, out-of-sample basis is a bigger challenge than many academics might assume, due to factors such as data revisions and changes in economic relationships over time. The paper stresses the value of both simulated real-time analysis -- looking at what the inference of a proposed model would have been using data as they were actually released at the time -- and actual real-time analysis, in which a researcher stakes his or her reputation on publicly using the model to generate out-of-sample, real-time predictions. The immediate publication capabilities of the internet make the latter a realistic option for researchers today, and many are taking advantage of it. The paper reviews a number of approaches to dating business cycle turning points and emphasizes the fundamental trade-off between parsimony -- trying to keep the model as simple and robust as possible -- and making full use of the available information. Different approaches have different advantages, and the paper concludes that there may be gains from combining the best features of several different approaches.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 4 (October)
Pages: 1006-1026

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Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1006-1026

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Web page: http://www.elsevier.com/locate/ijforecast

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Keywords: Business cycles Turning points Real-time data Markov-switching;

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References

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  1. Vladimir Yankov & Egon Zakrajsek & Simon Gilchrist, 2009. "Credit Market Shocks and Economic Fluctuations: Evidence from Corporate Bond and Stock Markets," 2009 Meeting Papers 514, Society for Economic Dynamics.
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  8. James D. Hamilton & Michael T. Owyang, 2009. "The propagation of regional recessions," Working Papers 2009-013, Federal Reserve Bank of St. Louis.
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  11. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc.
  12. Marcelle Chauvet & Simon Potter, 2001. "Forecasting recessions using the yield curve," Staff Reports 134, Federal Reserve Bank of New York.
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  14. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
  15. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
  16. Jonathan H. Wright, 2006. "The yield curve and predicting recessions," Finance and Economics Discussion Series 2006-07, Board of Governors of the Federal Reserve System (U.S.).
  17. 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 357-84, March.
  18. Maximo Camacho & Gabriel Perez-Quiros & Pilar Poncela, 2010. "Green shoots in the euro area. A real time measure," Banco de Espa�a Working Papers 1026, Banco de Espa�a.
  19. Heikki Kauppi & Pentti Saikkonen, 2008. "Predicting U.S. Recessions with Dynamic Binary Response Models," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 777-791, November.
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