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

Author

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  • Turgut Kisinbay
  • Chikako Baba

Abstract

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.

Suggested Citation

  • Turgut Kisinbay & Chikako Baba, 2011. "Predicting Recessions: A New Approach for Identifying Leading Indicators and Forecast Combinations," IMF Working Papers 2011/235, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:2011/235
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    References listed on IDEAS

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    Cited by:

    1. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    2. Donadelli, Michael & Paradiso, Antonio & Riedel, Max, 2015. "A novel ex-ante leading indicator for the EU industrial production," SAFE Working Paper Series 118, Leibniz Institute for Financial Research SAFE.
    3. Michael Donadelli & Antonio Paradiso & Max Riedel, 2019. "A Quasi Real‐Time Leading Indicator for the EU Industrial Production," Manchester School, University of Manchester, vol. 87(4), pages 510-542, July.

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