IDEAS home Printed from https://ideas.repec.org/p/bdp/dpaper/0046.html
   My bibliography  Save this paper

Detecting excessive credit growth: An approach based on structural counterfactuals

Author

Listed:
  • Magnus Saß

Abstract

The Basel credit-to-GDP gap is the single most popular measure of excessive credit growth and the financial cycle in general. It is based, however, on a purely statistical understanding of excessiveness: Growth is excessive if the credit-to-GDP ratio (i.e. the ratio of credit to nominal GDP) is significantly above its long-term trend. This paper presents an alternative approach where variation in the credit-to-GDP ratio is decomposed into its structural economic drivers. Some of these economic drivers are assumed to be non-excessive (aggregate demand and supply shocks), and others to be potentially excessive (all other shocks). Based on this identification, I construct a more structural credit gap measure that quantifies the impact of excessive drivers. In an early-warning exercise, I show that this gap measure performs particulary well in predicting financial crises at relatively short horizons.

Suggested Citation

  • Magnus Saß, 2024. "Detecting excessive credit growth: An approach based on structural counterfactuals," Berlin School of Economics Discussion Papers 0046, Berlin School of Economics.
    • Handle: RePEc:bdp:dpaper:0046
    DOI: 10.48462/opus4-5591
    as

    Download full text from publisher

    File URL: https://opus4.kobv.de/opus4-hsog/files/5591/BSoE_DP_0046.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.48462/opus4-5591?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gerhard Rünstler & Marente Vlekke, 2018. "Business, housing, and credit cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 212-226, March.
    2. Cottarelli, Carlo & Dell'Ariccia, Giovanni & Vladkova-Hollar, Ivanna, 2005. "Early birds, late risers, and sleeping beauties: Bank credit growth to the private sector in Central and Eastern Europe and in the Balkans," Journal of Banking & Finance, Elsevier, vol. 29(1), pages 83-104, January.
    3. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    4. Alessi, Lucia & Detken, Carsten, 2018. "Identifying excessive credit growth and leverage," Journal of Financial Stability, Elsevier, vol. 35(C), pages 215-225.
    5. Drehmann, Mathias & Juselius, Mikael, 2014. "Evaluating early warning indicators of banking crises: Satisfying policy requirements," International Journal of Forecasting, Elsevier, vol. 30(3), pages 759-780.
    6. Jonas E. Arias & Juan Rubio-Ramirez & Daniel F. Waggoner, 2013. "Inference Based on SVARs Identied with Sign and Zero Restrictions: Theory and Applications," Working Papers 2013-24, FEDEA.
    7. Buncic, Daniel & Melecky, Martin, 2014. "Equilibrium credit: The reference point for macroprudential supervisors," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 135-154.
    8. Tobias Knedlik & Gregor Von Schweinitz, 2012. "Macroeconomic Imbalances as Indicators for Debt Crises in Europe," Journal of Common Market Studies, Wiley Blackwell, vol. 50(5), pages 726-745, September.
    9. 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.
    10. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    11. Alessi, Lucia & Detken, Carsten, 2011. "Quasi real time early warning indicators for costly asset price boom/bust cycles: A role for global liquidity," European Journal of Political Economy, Elsevier, vol. 27(3), pages 520-533, September.
    12. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    13. Luca Gambetti & Alberto Musso, 2017. "Loan Supply Shocks and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 764-782, June.
    14. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    15. Canova, Fabio & Nicolo, Gianni De, 2002. "Monetary disturbances matter for business fluctuations in the G-7," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1131-1159, September.
    16. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    17. Andrea Gerali & Stefano Neri & Luca Sessa & Federico M. Signoretti, 2010. "Credit and Banking in a DSGE Model of the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(s1), pages 107-141, September.
    18. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    19. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    20. James D. Hamilton, 2018. "Why You Should Never Use the Hodrick-Prescott Filter," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 831-843, December.
    21. Moritz Schularick & Alan M. Taylor, 2012. "Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870-2008," American Economic Review, American Economic Association, vol. 102(2), pages 1029-1061, April.
    22. Christiano, Lawrence & Motto, Roberto & Rostagno, Massimo, 2010. "Financial factors in economic fluctuations," Working Paper Series 1192, European Central Bank.
    23. Mathias Drehmann & Claudio Borio & Kostas Tsatsaronis, 2011. "Anchoring Countercyclical Capital Buffers: The role of Credit Aggregates," International Journal of Central Banking, International Journal of Central Banking, vol. 7(4), pages 189-240, December.
    24. Philip Lowe & Claudio Borio, 2002. "Asset prices, financial and monetary stability: exploring the nexus," BIS Working Papers 114, Bank for International Settlements.
    25. El-Shagi, M. & Knedlik, T. & von Schweinitz, G., 2013. "Predicting financial crises: The (statistical) significance of the signals approach," Journal of International Money and Finance, Elsevier, vol. 35(C), pages 76-103.
    26. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    27. Mikael Juselius & Mathias Drehmann, 2015. "Leverage dynamics and the real burden of debt," BIS Working Papers 501, Bank for International Settlements.
    28. Markus Holopainen & Peter Sarlin, 2017. "Toward robust early-warning models: a horse race, ensembles and model uncertainty," Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1933-1963, December.
    29. Mathias Drehmann & James Yetman, 2018. "Why you should use the Hodrick-Prescott filter - at least to generate credit gaps," BIS Working Papers 744, Bank for International Settlements.
    30. Afanasyeva, Elena, 2013. "Atypical behavior of credit: Evidence from a monetary VAR," IMFS Working Paper Series 70, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    31. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    32. Gertler, Mark & Karadi, Peter, 2011. "A model of unconventional monetary policy," Journal of Monetary Economics, Elsevier, vol. 58(1), pages 17-34, January.
    33. Albuquerque Bruno & Baumann Ursel & Krustev Georgi, 2015. "US household deleveraging following the Great Recession – a model-based estimate of equilibrium debt," The B.E. Journal of Macroeconomics, De Gruyter, vol. 15(1), pages 255-307, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mugrabi, Farah & Rünstler, Gerhard, 2025. "Housing and Credit Cycles in Ireland," Research Technical Papers 16/RT/25, Central Bank of Ireland.
    2. Beltran, Daniel O. & Dalal, Vihar M. & Jahan-Parvar, Mohammad R. & Paine, Fiona A., 2024. "Optimizing composite early warning indicators," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    3. Lang, Jan Hannes & Welz, Peter, 2018. "Semi-structural credit gap estimation," Working Paper Series 2194, European Central Bank.
    4. Tore Dubbert & Adrian Schroeder, 2025. "Conditioning business and financial cycles on multivariate information," CQE Working Papers 11225, Center for Quantitative Economics (CQE), University of Muenster.
    5. Ponomarenko, Alexey & Tatarintsev, Stas, 2023. "Incorporating financial development indicators into early warning systems," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    6. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2018. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," Discussion Papers 48/2018, Deutsche Bundesbank.
    7. Zsuzsanna Hosszu & Gergely Lakos, 2022. "Early Warning Performance of Univariate Credit-to-GDP Gaps," MNB Occasional Papers 2022/142, Magyar Nemzeti Bank (Central Bank of Hungary).
    8. Barauskaitė, Kristina & Nguyen, Anh D.M. & Rousová, Linda & Cappiello, Lorenzo, 2022. "The impact of credit supply shocks in the euro area: market-based financing versus loans," Working Paper Series 2673, European Central Bank.
    9. Gregor Bäurle & Rolf Scheufele, 2019. "Credit cycles and real activity: the Swiss case," Empirical Economics, Springer, vol. 56(6), pages 1939-1966, June.
    10. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
    11. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    12. Mathias Drehmann & James Yetman, 2021. "Which Credit Gap Is Better at Predicting Financial Crises? A Comparison of Univariate Filters," International Journal of Central Banking, International Journal of Central Banking, vol. 17(70), pages 1-31, October.
    13. O'Brien, Martin & Velasco, Sofia, 2020. "Unobserved components models with stochastic volatility for extracting trends and cycles in credit," Research Technical Papers 09/RT/20, Central Bank of Ireland.
    14. Helene Olsen & Harald Wieslander, 2020. "The Impact of Monetary Policy on Leading Variables for Financial Stability in Norway," Working Papers No 02/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    15. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    16. Dieckelmann, Daniel, 2020. "Cross-border lending and the international transmission of banking crises," Discussion Papers 2020/13, Free University Berlin, School of Business & Economics.
    17. Daniel O. Beltran & Mohammad R. Jahan-Parvar & Fiona A. Paine, 2021. "Optimizing Credit Gaps for Predicting Financial Crises: Modelling Choices and Tradeoffs," International Finance Discussion Papers 1307, Board of Governors of the Federal Reserve System (U.S.).
    18. Mokinski, Frieder, 2017. "A severity function approach to scenario selection," Discussion Papers 34/2017, Deutsche Bundesbank.
    19. Huynh, Tran & Uebelmesser, Silke, 2024. "Early warning models for systemic banking crises: Can political indicators improve prediction?," European Journal of Political Economy, Elsevier, vol. 81(C).
    20. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G01 - Financial Economics - - General - - - Financial Crises
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdp:dpaper:0046. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christian Reiter (email available below). General contact details of provider: https://edirc.repec.org/data/bdpemde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.