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A tale of two recession-derivative indicators

Author

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  • Kajal Lahiri

    (University at Abany: SUNY)

  • Cheng Yang

    (Liaoning University)

Abstract

Two recession-derivative indicators (RDIs) have been used extensively as forecast objects in business cycle prediction, viz. (1) the target variable takes value 1 if there is a recession starting exactly at a specific horizon in the future, and (2) the target variable takes value 1 if there is a recession starting any time over a specified period in the future. Using daily yield spread as an illustrative predictor, we formally and quantitatively compare the two RDIs using the receiver operating characteristics analysis. Over 1962–2021 covering eight NBER recessions, we find that generally the second RDI, ceteris paribus, will make the the predictor better performing. However, the first RDI can generate better-looking and more useful predictions under certain scenarios, depending on forecast horizon, recession duration and time profile of signals. We also consider a semiannual chronology proposed by Peláez (J Macroecon 45:384–393, 2015) and find that its performance is in the middle of the other two. Our analysis suggests that the choice of a particular RDI should be dictated by the needs of forecast user in a particular decision making context.

Suggested Citation

  • Kajal Lahiri & Cheng Yang, 2023. "A tale of two recession-derivative indicators," Empirical Economics, Springer, vol. 65(2), pages 925-947, August.
    • Handle: RePEc:spr:empeco:v:65:y:2023:i:2:d:10.1007_s00181-023-02361-6
    DOI: 10.1007/s00181-023-02361-6
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    1. Don Harding & Adrian Pagan, 2016. "The Econometric Analysis of Recurrent Events in Macroeconomics and Finance," Economics Books, Princeton University Press, edition 1, number 10744.
    2. Fabio Moneta, 2005. "Does the Yield Spread Predict Recessions in the Euro Area?," International Finance, Wiley Blackwell, vol. 8(2), pages 263-301, August.
    3. Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-277, April.
    4. Francis Bismans & Reynald Majetti, 2013. "Forecasting recessions using financial variables : the French case," Post-Print hal-01753806, HAL.
    5. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators, and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1, March.
    6. Francis Bismans & Reynald Majetti, 2013. "Forecasting recessions using financial variables: the French case," Empirical Economics, Springer, vol. 44(2), pages 419-433, April.
    7. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    8. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    9. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    10. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    11. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    12. Yi-Ting Chen, 2021. "A mixed-frequency smooth measure for business conditions," Empirical Economics, Springer, vol. 61(4), pages 1699-1724, October.
    13. Michael D. Bauer & Thomas M. Mertens, 2018. "Information in the Yield Curve about Future Recessions," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    14. Ralf Fendel & Nicola Mai & Oliver Mohr, 2021. "Recession probabilities for the Eurozone at the zero lower bound: Challenges to the term spread and rise of alternatives," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1000-1026, September.
    15. Daniel H. Cooper & Jeffrey C. Fuhrer & Giovanni P. Olivei, 2020. "Predicting Recessions Using the Yield Curve: The Role of the Stance of Monetary Policy," Current Policy Perspectives 87522, Federal Reserve Bank of Boston.
    16. Lahiri, Kajal & Yang, Liu, 2015. "A further analysis of the conference board’s new Leading Economic Index," International Journal of Forecasting, Elsevier, vol. 31(2), pages 446-453.
    17. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    18. Duarte, Agustin & Venetis, Ioannis A. & Paya, Ivan, 2005. "Predicting real growth and the probability of recession in the Euro area using the yield spread," International Journal of Forecasting, Elsevier, vol. 21(2), pages 261-277.
    19. Borio, Claudio & Drehmann, Mathias & Xia, Fan Dora, 2020. "Forecasting recessions: the importance of the financial cycle," Journal of Macroeconomics, Elsevier, vol. 66(C).
    20. Bellégo, C. & Ferrara, L., 2009. "Forecasting Euro-area recessions using time-varying binary response models for financial," Working papers 259, Banque de France.
    21. Ng, Eric C.Y., 2012. "Forecasting US recessions with various risk factors and dynamic probit models," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 112-125.
    22. Peter Johansson & Andrew C. Meldrum, 2018. "Predicting Recession Probabilities Using the Slope of the Yield Curve," FEDS Notes 2018-03-01-3, Board of Governors of the Federal Reserve System (U.S.).
    23. David S. Miller, 2019. "There is No Single Best Predictor of Recessions," FEDS Notes 2019-05-21-2, Board of Governors of the Federal Reserve System (U.S.).
    24. Andrea Ajello & Luca Benzoni & Makena Schwinn & Yannick Timmer & Francisco Vazquez-Grande, 2022. "Monetary Policy, Inflation Outlook, and Recession Probabilities," FEDS Notes 2022-07-12, Board of Governors of the Federal Reserve System (U.S.).
    25. Huiwen Lai & Eric C. Y. Ng, 2020. "On business cycle forecasting," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-26, December.
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    Cited by:

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    More about this item

    Keywords

    Business cycle; NBER; Yield spread; ROC; Recession; Youden index;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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