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Forecasting recessions with time-varying models

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  • Hwang, Youngjin

Abstract

This study presents a flexible recession forecast model where predictive variables and model coefficients can vary over time. In an application to US recession forecasting using pseudo real-time data, we find that time-varying logit models lead to large improvements in forecast performance, beating the individual best predictors as well as other popular alternative methods. Through these results, we also demonstrate the following features of the forecast models: (i) substituting roles between the two key features of predictor switching and coefficient change, (ii) considerable variations in the model size (i.e., the number of predictors used) over time, and (iii) substantial changes in the role/importance of major individual predictors over business cycles.

Suggested Citation

  • Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:jmacro:v:62:y:2019:i:c:s0164070419300758
    DOI: 10.1016/j.jmacro.2019.103153
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    Cited by:

    1. Hasse, Jean-Baptiste & Lajaunie, Quentin, 2022. "Does the yield curve signal recessions? New evidence from an international panel data analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 9-22.
    2. Kajal Lahiri & Cheng Yang, 2023. "A tale of two recession-derivative indicators," Empirical Economics, Springer, vol. 65(2), pages 925-947, August.
    3. Massimo Ferrari Minesso & Laura Lebastard & Helena Mezo, 2023. "Text-Based Recession Probabilities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 415-438, June.
    4. Okimoto, Tatsuyoshi & Takaoka, Sumiko, 2022. "The credit spread curve distribution and economic fluctuations in Japan," Journal of International Money and Finance, Elsevier, vol. 122(C).
    5. Leakey Omolo & Nguyet Nguyen, 2024. "Using an Ensemble of Machine Learning Algorithms to Predict Economic Recession," JRFM, MDPI, vol. 17(9), pages 1-26, September.
    6. Zihao Wang & Kun Li & Steve Q. Xia & Hongfu Liu, 2021. "Economic Recession Prediction Using Deep Neural Network," Papers 2107.10980, arXiv.org.
    7. Borio, Claudio & Drehmann, Mathias & Xia, Fan Dora, 2020. "Forecasting recessions: the importance of the financial cycle," Journal of Macroeconomics, Elsevier, vol. 66(C).
    8. Glocker, Christian & Kaniovski, Serguei, 2020. "Structural modeling and forecasting using a cluster of dynamic factor models," MPRA Paper 101874, University Library of Munich, Germany.
    9. Xing, Kai & Luo, Dan & Liu, Lanlan, 2023. "Macroeconomic conditions, corporate default, and default clustering," Economic Modelling, Elsevier, vol. 118(C).
    10. Seulki Chung, 2023. "Real-time Prediction of the Great Recession and the Covid-19 Recession," Papers 2310.08536, arXiv.org, revised May 2024.

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    More about this item

    Keywords

    Recession forecasting; Real-time data; Dynamic model averaging/selection; Time-varying coefficients;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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