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Credit-to-GDP Gap Estimates in Real Time: A Stable Indicator for Macroprudential Policy Making in Croatia

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  • Tihana Škrinjarić

    (Bank of England)

Abstract

Macroprudential policymakers track cyclical risk accumulation via a wide range of indicators. To make timely policy decisions, these indicators need to be valid, stable and a good representation of (future) financial cycle movements. The Basel gap is the most commonly used indicator in the EU, as it is a part of the Basel III regulatory framework as a standardized and harmonized indicator. Countercyclical capital buffer (CCyB) calibration is one of several macroprudential policy concepts based on the Basel gap. However, due to the endpoint problem of the Hodrick–Prescott (HP) filter to the estimation of the Basel gap, CCyB calibration remains a challenge. This study focuses on defining a clear set of criteria that can be used to solve the endpoint problem of the filtering process. This approach is appropriate for authorities whose analysis shows that the HP based indicators are the best in predicting financial crisis. The results of this study can be used in real-time decision-making, as they are relatively simple to estimate and communicate. Such augmented gaps reduce the bias in the gap series after turning points in the financial cycle.

Suggested Citation

  • Tihana Škrinjarić, 2023. "Credit-to-GDP Gap Estimates in Real Time: A Stable Indicator for Macroprudential Policy Making in Croatia," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 65(3), pages 582-614, September.
    • Handle: RePEc:pal:compes:v:65:y:2023:i:3:d:10.1057_s41294-023-00220-y
    DOI: 10.1057/s41294-023-00220-y
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    as
    1. 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.
    2. Deryugina, Elena & Ponomarenko, Alexey & Rozhkova, Anna, 2020. "When are credit gap estimates reliable?," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 221-238.
    3. Canova, Fabio, 1998. "Detrending and business cycle facts: A user's guide," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 533-540, May.
    4. Hristov, Nikolay & Roth, Markus, 2022. "Uncertainty shocks and systemic-risk indicators," Journal of International Money and Finance, Elsevier, vol. 122(C).
    5. Canova, Fabio, 1998. "Detrending and business cycle facts," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 475-512, May.
    6. 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.
    7. Günes Kamber & James Morley & Benjamin Wong, 2018. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 550-566, July.
    8. Babecký, Jan & Havránek, Tomáš & Matějů, Jakub & Rusnák, Marek & Šmídková, Kateřina & Vašíček, Bořek, 2014. "Banking, debt, and currency crises in developed countries: Stylized facts and early warning indicators," Journal of Financial Stability, Elsevier, vol. 15(C), pages 1-17.
    9. Soojin Jo & Rodrigo Sekkel, 2019. "Macroeconomic Uncertainty Through the Lens of Professional Forecasters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 436-446, July.
    10. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    11. 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.
    12. Pierre St-Amant & Simon van Norden, 1997. "Measurement of the Output Gap: A Discussion of Recent Research at the Bank of Canada," Technical Reports 79, Bank of Canada.
    13. Regina Kaiser & Agustín Maravall, 1999. "Estimation of the business cycle: A modified Hodrick-Prescott filter," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(2), pages 175-206.
    14. Terhi Jokipii & Reto Nyffeler & Stéphane Riederer, 2021. "Exploring BIS credit-to-GDP gap critiques: the Swiss case," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 157(1), pages 1-19, December.
    15. Barbara Rossi & Tatevik Sekhposyan, 2015. "Macroeconomic Uncertainty Indices Based on Nowcast and Forecast Error Distributions," American Economic Review, American Economic Association, vol. 105(5), pages 650-655, May.
    16. Rochelle M. Edge & Ralf R. Meisenzahl, 2011. "The unreliability of credit-to-GDP ratio gaps in real-time: Implications for countercyclical capital buffers," Finance and Economics Discussion Series 2011-37, Board of Governors of the Federal Reserve System (U.S.).
    17. Saleem Bahaj & Angus Foulis, 2017. "Macroprodential Policy under Uncertainty," International Journal of Central Banking, International Journal of Central Banking, vol. 13(3), pages 119-154, September.
    18. Pedersen, Torben Mark, 2001. "The Hodrick-Prescott filter, the Slutzky effect, and the distortionary effect of filters," Journal of Economic Dynamics and Control, Elsevier, vol. 25(8), pages 1081-1101, August.
    19. Cogley, Timothy & Nason, James M, 1995. "Output Dynamics in Real-Business-Cycle Models," American Economic Review, American Economic Association, vol. 85(3), pages 492-511, June.
    20. Davis, E. Philip & Karim, Dilruba, 2008. "Comparing early warning systems for banking crises," Journal of Financial Stability, Elsevier, vol. 4(2), pages 89-120, June.
    21. repec:cup:judgdm:v:5:y:2010:i:6:p:458-466 is not listed on IDEAS
    22. 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.
    23. Istiak, Khandokar & Serletis, Apostolos, 2020. "Risk, uncertainty, and leverage," Economic Modelling, Elsevier, vol. 91(C), pages 257-273.
    24. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    25. Scotti, Chiara, 2016. "Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 1-19.
    26. Aikman, David & Bridges, Jonathan & Hacioglu Hoke, Sinem & O’Neill, Cian & Raja, Akash, 2019. "Credit, capital and crises: a GDP-at-Risk approach," Bank of England working papers 824, Bank of England, revised 18 Oct 2019.
    27. Adam Geršl & Thomas Mitterling, 2021. "Forecast-Augmented Credit-to-GDP Gap as an Early Warning Indicator of Banking Crises," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 71(4), pages 323-351, December.
    28. 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.
    29. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    30. De Nora, Giorgia & O'Brien, Eoin & O'Brien, Martin, 2020. "Releasing the CCyB to support the economy in a time of stress," Financial Stability Notes 1/FS/20, Central Bank of Ireland.
    31. Piergiorgio Alessandri & Pierluigi Bologna & Roberta Fiori & Enrico Sette, 2015. "A note on the implementation of the countercyclical capital buffer in Italy," Questioni di Economia e Finanza (Occasional Papers) 278, Bank of Italy, Economic Research and International Relations Area.
    32. Pierre-Olivier Gourinchas & Maurice Obstfeld, 2012. "Stories of the Twentieth Century for the Twenty-First," American Economic Journal: Macroeconomics, American Economic Association, vol. 4(1), pages 226-265, January.
    33. 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.
    34. Erlend Nier & Mr. Thorvardur Tjoervi Olafsson & Yuan Gao Rollinson, 2020. "Exchange Rates and Domestic Credit—Can Macroprudential Policy Reduce the Link?," IMF Working Papers 2020/187, International Monetary Fund.
    35. Tiago M. Fragoso & Wesley Bertoli & Francisco Louzada, 2018. "Bayesian Model Averaging: A Systematic Review and Conceptual Classification," International Statistical Review, International Statistical Institute, vol. 86(1), pages 1-28, April.
    36. Amélie Charles & Olivier Darné & Fabien Tripier, 2018. "Uncertainty and the macroeconomy: evidence from an uncertainty composite indicator," Applied Economics, Taylor & Francis Journals, vol. 50(10), pages 1093-1107, February.
    37. Charemza, Wojciech & Ladley, Daniel, 2016. "Central banks’ forecasts and their bias: Evidence, effects and explanation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 804-817.
    38. J. M. Keynes, 1937. "The General Theory of Employment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 51(2), pages 209-223.
    39. Bertrand Candelon & Elena-Ivona Dumitrescu & Christophe Hurlin, 2012. "How to Evaluate an Early-Warning System: Toward a Unified Statistical Framework for Assessing Financial Crises Forecasting Methods," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(1), pages 75-113, April.
    40. Mise, Emi & Kim, Tae-Hwan & Newbold, Paul, 2005. "On suboptimality of the Hodrick-Prescott filter at time series endpoints," Journal of Macroeconomics, Elsevier, vol. 27(1), pages 53-67, March.
    41. Mawuli Segnon & Rangan Gupta & Stelios Bekiros & Mark E. Wohar, 2018. "Forecasting US GNP growth: The role of uncertainty," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 541-559, August.
    42. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    43. Torsten Wezel, 2019. "Conceptual Issues in Calibrating the Basel III Countercyclical Capital Buffer," IMF Working Papers 2019/086, International Monetary Fund.
    44. Callum Jones & Mr. Pau Rabanal, 2021. "Credit Cycles, Fiscal Policy, and Global Imbalances," IMF Working Papers 2021/043, International Monetary Fund.
    45. 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.
    46. Runde, Jochen, 1998. "Clarifying Frank Knight's Discussion of the Meaning of Risk and Uncertainty," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 22(5), pages 539-546, September.
    47. Rochelle M. Edge & Ralf R. Meisenzahl, 2011. "The Unreliability of Credit-to-GDP Ratio Gaps in Real Time: Implications for Countercyclical Capital Buffers," International Journal of Central Banking, International Journal of Central Banking, vol. 7(4), pages 261-298, December.
    48. Claudio Borio & Mathias Drehmann, 2009. "Assessing the risk of banking crises - revisited," BIS Quarterly Review, Bank for International Settlements, March.
    49. Mathias Drehmann & Claudio Borio & Leonardo Gambacorta & Gabriel Jiminez & Carlos Trucharte, 2010. "Countercyclical capital buffers: exploring options," BIS Working Papers 317, Bank for International Settlements.
    50. Claudio Borio & Philip Lowe, 2002. "Assessing the risk of banking crises," BIS Quarterly Review, Bank for International Settlements, December.
    51. O'Brien, Martin & Wosser, Michael, 2021. "Growth at Risk and Financial Stability," Financial Stability Notes 2/FS/21, Central Bank of Ireland.
    52. Klomp, Jeroen, 2010. "Causes of banking crises revisited," The North American Journal of Economics and Finance, Elsevier, vol. 21(1), pages 72-87, March.
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    More about this item

    Keywords

    Credit-to-GDP gap; Credit gap augmentation; Countercyclical capital buffer; Out-of-sample forecasting;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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