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Predicting Recessions: A New Approach for Identifying Leading Indicators and Forecast Combinations

  • Turgut Kisinbay
  • Chikako Baba

This study proposes a data-based algorithm to select a subset of indicators from a large data set with a focus on forecasting recessions. The algorithm selects leading indicators of recessions based on the forecast encompassing principle and combines the forecasts. An application to U.S. data shows that forecasts obtained from the algorithm are consistently among the best in a large comparative forecasting exercise at various forecasting horizons. In addition, the selected indicators are reasonable and consistent with the standard leading indicators followed by many observers of business cycles. The suggested algorithm has several advantages, including wide applicability and objective variable selection.

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Paper provided by International Monetary Fund in its series IMF Working Papers with number 11/235.

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Length: 30
Date of creation: 01 Oct 2011
Date of revision:
Handle: RePEc:imf:imfwpa:11/235
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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. Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-77, April.
  3. Hamilton, James D., 2011. "Calling recessions in real time," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1006-1026, October.
  4. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  5. Edward E. Leamer, 2007. "Housing IS the Business Cycle," NBER Working Papers 13428, National Bureau of Economic Research, Inc.
  6. Galbraith, John W. & van Norden, Simon, 2011. "Kernel-based calibration diagnostics for recession and inflation probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1041-1057, October.
  7. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
  8. Loungani, Prakash, 2001. "How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth," International Journal of Forecasting, Elsevier, vol. 17(3), pages 419-432.
  9. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
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