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Evaluating the predicting power of ordered probit models for multiple business cycle phases in the U.S. and Japan

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  • Proaño, Christian R.
  • Tarassow, Artur

Abstract

We investigate the probability forecasting performance of a three-regime dynamic ordered probit econometric framework suitable to forecast recessions, low growth periods and accelerations for the U.S. and Japan. In a first step, we apply a non-parametric dating algorithm for the identification of these three phases along the lines of Proaño (2017). We compare the pseudo-out-of-sample forecasting skills of an otherwise standard binary dynamic probit model with a three-regime dynamic ordered probit framework by means of a rolling-window exercise combined with time-varying indicator selection. Based on a set of monthly macroeconomic and financial leading indicators, the results show the superiority of the ordered probit framework to forecast all three business cycle phases up to six months ahead under real-time conditions. Receiver operating characteristics and related summarizing statistics are applied as probability forecast evaluation measures.

Suggested Citation

  • Proaño, Christian R. & Tarassow, Artur, 2018. "Evaluating the predicting power of ordered probit models for multiple business cycle phases in the U.S. and Japan," Journal of the Japanese and International Economies, Elsevier, vol. 50(C), pages 60-71.
    • Handle: RePEc:eee:jjieco:v:50:y:2018:i:c:p:60-71
    DOI: 10.1016/j.jjie.2018.08.002
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    More about this item

    Keywords

    Forecasting; Recession; Stagnation; ROC;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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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