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Unobserved components model estimates of credit cycles: Tests and predictions

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  • Hessler, Andrew

Abstract

This paper estimates unobserved components (UC) models with real and financial trends and business and credit cycles to assess different measures of the credit cycle used by policymakers. The permanent components of the real and financial sectors are a Beveridge–Nelson and local linear trend, respectively. The business and credit cycles evolve jointly as a second-order vector autoregression. Bootstrap methods are applied to UC model estimates retrieved from classical optimization of the predictive likelihood of the Kalman filter. Results indicate the slope of the financial trend better predicts the credit to GDP ratio in the United States than the estimated business and credit cycles and the Basel gap. This suggests policymakers should consider permanent shocks to the financial sector when gauging the state of financial stability.

Suggested Citation

  • Hessler, Andrew, 2023. "Unobserved components model estimates of credit cycles: Tests and predictions," Journal of Financial Stability, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:finsta:v:66:y:2023:i:c:s1572308923000207
    DOI: 10.1016/j.jfs.2023.101120
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    More about this item

    Keywords

    Business cycle; Credit cycle; Unobserved components model; Bootstrap;
    All these keywords.

    JEL classification:

    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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