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Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors

Author

Listed:
  • Yang Aijun

    (Nanjing Forestry University
    Southeast University)

  • Xiang Ju

    (South University of Science and Technology of China)

  • Yang Hongqiang

    (Nanjing Forestry University)

  • Lin Jinguan

    (Nanjing Audit University)

Abstract

In this paper, a large set of macroeconomic and financial predictors is used to forecast U.S. recession periods. We propose a sparse Bayesian variable selection in probit model for predicting U.S. recessions. The correlation prior is assigned for the binary vector to distinguish models with the same size, and the sparse prior is specified for the coefficient parameters for the purpose of predicting accurately using fewer parameters. In terms of the quadratic probability score and the log probability score, we demonstrate that the proposed method performs better than other three methods.

Suggested Citation

  • Yang Aijun & Xiang Ju & Yang Hongqiang & Lin Jinguan, 2018. "Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1123-1138, April.
    • Handle: RePEc:kap:compec:v:51:y:2018:i:4:d:10.1007_s10614-017-9660-1
    DOI: 10.1007/s10614-017-9660-1
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    References listed on IDEAS

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    Cited by:

    1. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    2. Gil Cohen, 2021. "Optimizing Algorithmic Strategies for Trading Bitcoin," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 639-654, February.
    3. Aijun Yang & Yuzhu Tian & Yunxian Li & Jinguan Lin, 2020. "Sparse Bayesian variable selection in kernel probit model for analyzing high-dimensional data," Computational Statistics, Springer, vol. 35(1), pages 245-258, March.

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