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Viewpoint: Boosting Recessions

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  • Serena Ng

Abstract

This paper explores the effectiveness of boosting, often regarded as the state of the art classification tool, in giving warning signals of recessions 3, 6, and 12 months ahead. Boosting is used to screen as many as 1,500 potentially relevant predictors consisting of 132 real and financial time series and their lags. Estimation over the full sample 1961:12011:12 finds that there are fewer than 10 important predictors and the identity of these variables changes with the forecast horizon. There is a distinct difference in the size and composition of the relevant predictor set before and after mid1980. Rolling window estimation reveals that the importance of the term and default spreads are recession specific. The Aaa spread is the most robust predictor of recessions three and 6 months ahead, while the risky bond and 5year spreads are important for 12 months ahead predictions. Certain employment variables have predictive power for the two most recent recessions when the interest rate spreads were uninformative. Warning signals for the post1990 recessions have been sporadic and easy to miss. The results underscore the challenge that changing characteristics of business cycles pose for predicting recessions.

Suggested Citation

  • Serena Ng, 2014. "Viewpoint: Boosting Recessions," Canadian Journal of Economics, Canadian Economics Association, vol. 47(1), pages 1-34, February.
    • Handle: RePEc:cje:issued:v:47:y:2014:i:1:p:1-34
    DOI: 10.1111/caje.12070
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    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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