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The credit-to-GDP gap data set aims at quantifying the notion of “excessive credit” in a simple way. It serves as an early warning indicator for potential banking crises or severe distress.

The data set covers 44 economies, starting at earliest in 1961 and captures total borrowing from all domestic and foreign sources. The credit-to-GDP gap is defined as the difference between the credit-to-GDP ratio and its long-run trend. The trend is derived using a one-sided (ie backward-looking) Hodrick-Prescott filter.

To facilitate comparability across countries, the credit-to-GDP ratio, as published in the BIS database of total credit to the private non-financial sector, is used as input data. However, it also means that the credit-to-GDP gaps published by the BIS may differ from those used by national authorities as part of their countercyclical capital buffer decisions.

The gap indicator was adopted as a common reference point under Basel III to guide the build-up of countercyclical capital buffers. Authorities are expected, however, to apply judgment in the setting of the buffer in their jurisdiction after using the best information available to gauge the build-up of system-wide risk rather than relying mechanistically on the credit-to-GDP guide. For instance, national authorities may form their policy decisions using credit-to-GDP ratios that are based on data series that differ from the BIS series, leading to credit-to-GDP gaps that differ from those published by the BIS.

For the technical features of the data set, see "Recent enhancements to the BIS statistics", BIS Quarterly Review, September 2016. Please also refer to the Basel Committee on Banking Supervision page on the countercyclical capital buffer.

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Methodology

Research and publications

Which credit gap is better at predicting financial crises? A comparison of univariate filters

The credit gap, defined as the deviation of the credit-to-GDP ratio from a one-sided HP-filtered trend, is a useful indicator for predicting financial crises. Basel III therefore suggests that policymakers use it as part of their countercyclical capital buffer frameworks. Hamilton (2018), however, argues that you should never use an HP filter as it results in spurious dynamics, has end-point problems and its typical implementation is at odds with its statistical foundations. Instead he proposes ...

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