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

  • Marco Del Negro

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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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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  1. 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.
  2. 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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  4. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  5. Eric M. Leeper & Tao Zha, 1999. "Modest policy interventions," Working Paper 99-22, Federal Reserve Bank of Atlanta.
  6. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
  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. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
  9. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, October.
  10. 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.
  11. Daniel M. Chin & John F. Geweke & Preston J. Miller, 2000. "Predicting turning points," Staff Report 267, Federal Reserve Bank of Minneapolis.
  12. Andrew J. Filardo, 1999. "How reliable are recession prediction models?," Economic Review, Federal Reserve Bank of Kansas City, issue Q II, pages 35-55.
  13. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
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  15. 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.
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