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Turn, turn, turn: Predicting turning points in economic activity

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  • Marco Del Negro

Abstract

Policy and investment decisions are made with an eye toward future economic conditions, and an econometric model that can correctly forecast directional changes in the business cycle would be a boon to policymakers, the business community, and the general public. This article provides some evidence on econometric models' ability to predict these directional changes, also known as turning points, in an effort to answer the question, How good is the state of the art in turning point forecasting? ; The author first discusses the definition of turning points and describes different approaches to turning point forecasting, along with their relative advantages and disadvantages. Next, the article assesses the performance of the Atlanta Fed Bayesian vector autoregression (BVAR) model in terms of forecasting turning points relative to a well-known alternative, the Leading Economic Indicators (LEI) Index. The author concludes that the BVAR model forecasts contain information on future recessions that appears superior to that embodied in the LEI Index, at least when simple rules of thumb are used to extract information from the index. ; Relative to a turning point model proposed by Arturo Estrella and Frederic Mishkin, however, the Atlanta Fed BVAR model is far less precise in indicating the exact timing of a recession. In general, the warning signals from models that are specifically designed to forecast turning points appear to be of better quality than those from econometric models like the BVAR model, suggesting that it is worthwhile to supplement the BVAR with a turning point model.

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Bibliographic Info

Article provided by Federal Reserve Bank of Atlanta in its journal Economic Review.

Volume (Year): (2001)
Issue (Month): Q2 ()
Pages: 1-12

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Handle: RePEc:fip:fedaer:y:2001:i:q2:p:1-12:n:v.86no.2

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Keywords: Business cycles ; Economic indicators ; Recessions ; Forecasting ; Econometric models;

References

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  1. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
  2. Arturo Estrella & Frederic S. Mishkin, 1995. "Predicting U.S. Recessions: Financial Variables as Leading Indicators," NBER Working Papers 5379, National Bureau of Economic Research, Inc.
  3. Estrella, Arturo & Hardouvelis, Gikas A, 1991. " The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-76, June.
  4. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
  5. Eric M. Leeper & Tao Zha, 1999. "Modest policy interventions," Working Paper 99-22, Federal Reserve Bank of Atlanta.
  6. 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.
  7. Don Harding & Adrian Pagan, 1999. "Knowing the Cycle," Melbourne Institute Working Paper Series wp1999n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  8. Tao Zha, 1998. "A dynamic multivariate model for use in formulating policy," Economic Review, Federal Reserve Bank of Atlanta, issue Q 1, pages 16-29.
  9. Neftici, Salih N., 1982. "Optimal prediction of cyclical downturns," Journal of Economic Dynamics and Control, Elsevier, vol. 4(1), pages 225-241, November.
  10. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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  12. Daniel M. Chin & John F. Geweke & Preston J. Miller, 2000. "Predicting turning points," Staff Report 267, Federal Reserve Bank of Minneapolis.
  13. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," The Journal of Business, University of Chicago Press, vol. 62(3), pages 369-91, July.
  14. repec:wop:humbsf:1999-4 is not listed on IDEAS
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  16. 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.
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Cited by:
  1. Rangan Gupta & Sonali Das, 2010. "Predicting Downturns in the US Housing Market: A Bayesian Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 41(3), pages 294-319, October.
  2. Francis Bismans & Reynald Majetti, 2013. "Forecasting recessions using financial variables: the French case," Empirical Economics, Springer, vol. 44(2), pages 419-433, April.
  3. Das, Sonali & Gupta, Rangan & Kabundi, Alain, 2009. "Could we have predicted the recent downturn in the South African housing market?," Journal of Housing Economics, Elsevier, vol. 18(4), pages 325-335, December.
  4. Moneta, Fabio, 2003. "Does the yield spread predict recessions in the euro area?," Working Paper Series 0294, European Central Bank.
  5. Wh Boshoff, 2005. "The Properties Of Cycles In South African Financial Variables And Their Relation To The Business Cycle," South African Journal of Economics, Economic Society of South Africa, vol. 73(4), pages 694-709, December.
  6. Donna K. Ginther & Madeline Zavodny, 2001. "The Beige Book: Timely information on the regional economy," Economic Review, Federal Reserve Bank of Atlanta, issue Q3, pages 19-29.

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