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

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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.

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Notes

  1. The NBER defines a recession as a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in production, employment, real income, and other indicators.

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Acknowledgements

The authors gratefully acknowledge the financial support of the Natural Science Foundation of China (11501294, 11571073), the China Postdoctoral Science Foundation (2015M580374, 2016T90398), the Natural Science Foundation of Jiangsu (BK20141326) and the Research Fund for the Doctoral Program of Higher Education of China (20120092110021).

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Authors and Affiliations

  1. College of Economics and Management, Nanjing Forestry University, Nanjing, China

    Yang Aijun & Yang Hongqiang

  2. School of Economics and Management, Southeast University, Nanjing, China

    Yang Aijun

  3. Department of Financial Mathematics and Financial Engineering, South University of Science and Technology of China, Shenzhen, China

    Xiang Ju

  4. Institute of Statistics and Big Data, Nanjing Audit University, Nanjing, China

    Lin Jinguan

Authors
  1. Yang Aijun
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  2. Xiang Ju
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  3. Yang Hongqiang
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  4. Lin Jinguan
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Corresponding author

Correspondence to Yang Aijun.

Appendix: Data description

Appendix: Data description

The data used in Sect. 3 are presented here. The format is as follows: name, transformation code, and brief series description. The transformation codes are \(1=\mathrm{no transformation}\), \(2=\mathrm{first difference}\), \(4=\mathrm{logarithm}\), \(5=\mathrm{first difference of logarithms}\), \(6=\mathrm{second difference of logarithms}\) (Table 4).

Table 4 Data descriptions
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Aijun, Y., Ju, X., Hongqiang, Y. et al. Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors. Comput Econ 51, 1123–1138 (2018). https://doi.org/10.1007/s10614-017-9660-1

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