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A Bivariate Autoregressive Probit Model: Business Cycle Linkages And Transmission Of Recession Probabilities

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  • Nyberg, Henri

Abstract

I propose a new binary bivariate autoregressive probit model of the state of the business cycle. This model nests various special cases, such as two separate univariate probit models used extensively in the previous literature. The parameters are estimated by the method of maximum likelihood and forecasts can be computed by explicit formulae. The model is applied to predict the U.S. and German business cycle recession and expansion periods. Evidence of in-sample and out-of-sample predictability of recession periods by financial variables is obtained. The proposed bivariate autoregressive probit model allowing links between the recession probabilities in the United States and Germany turns out to outperform two univariate models.

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  • Nyberg, Henri, 2014. "A Bivariate Autoregressive Probit Model: Business Cycle Linkages And Transmission Of Recession Probabilities," Macroeconomic Dynamics, Cambridge University Press, vol. 18(4), pages 838-862, June.
    • Handle: RePEc:cup:macdyn:v:18:y:2014:i:04:p:838-862_00
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    Cited by:

    1. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    2. Fornaro, Paolo, 2015. "Forecasting U.S. Recessions with a Large Set of Predictors," MPRA Paper 62973, University Library of Munich, Germany.
    3. Antunes, António & Bonfim, Diana & Monteiro, Nuno & Rodrigues, Paulo M.M., 2018. "Forecasting banking crises with dynamic panel probit models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 249-275.
    4. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    5. Baumann, Ursel & Gómez Salvador, Ramón & Seitz, Franz, 2018. "Global recessions and booms: What do probit models tell us?," Weidener Diskussionspapiere 61, University of Applied Sciences Amberg-Weiden (OTH).
    6. Marius M. Mihai, 2020. "Do credit booms predict US recessions?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 887-910, September.
    7. Harri Pönkä & Markku Stenborg, 2020. "Forecasting the state of the Finnish business cycle," Finnish Economic Papers, Finnish Economic Association, vol. 29(1), pages 81-99, Spring.
    8. Goodness C. Aye & Christina Christou & Luis A. Gil‐Alana & Rangan Gupta, 2019. "Forecasting the Probability of Recessions in South Africa: the Role of Decomposed Term Spread and Economic Policy Uncertainty," Journal of International Development, John Wiley & Sons, Ltd., vol. 31(1), pages 101-116, January.
    9. Harri Ponka, 2017. "The Role of Credit in Predicting US Recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 469-482, August.
    10. Mei-Chih Wang & Pao-Lan Kuo & Chan-Sheng Chen & Chien-Liang Chiu & Tsangyao Chang, 2020. "Yield Spread and Economic Policy Uncertainty: Evidence from Japan," Sustainability, MDPI, vol. 12(10), pages 1-14, May.
    11. Stanislav Anatolyev & Nikolay Gospodinov, 2019. "Multivariate Return Decomposition: Theory and Implications," Econometric Reviews, Taylor & Francis Journals, vol. 38(5), pages 487-508, May.
    12. Seulki Chung, 2023. "Inside the black box: Neural network-based real-time prediction of US recessions," Papers 2310.17571, arXiv.org, revised Mar 2024.
    13. Kar, Sabyasachi & Roy, Amrita & Sen, Kunal, 2019. "The double trap: Institutions and economic development," Economic Modelling, Elsevier, vol. 76(C), pages 243-259.
    14. Pönkä, Harri & Zheng, Yi, 2019. "The role of oil prices on the Russian business cycle," Research in International Business and Finance, Elsevier, vol. 50(C), pages 70-78.

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