As more data become available, the problem becomes less important for policymaking, although it remains an issue for the ex post assessment of the performance of the credit gap.Structural breaks in the credit-to-GDP series present similar challenges to the start point problem discussed above (FitchRatings (2010), World Bank (2010)). In particular, they suggest that the guide might trigger procyclical changes in the buffer, that is, lead to increases in bank capital during periods of recession and declines in periods of economic expansion.
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Geršl and Seidler (2012) point out that the trend calculation can depend significantly on the starting point of the data. The ideal indicator would signal all impending crises and never crises that fail to materialise. What would be a nice little equation for it? There are four possible value combinations of a binary signal (which can be "on" or "off") and subsequent event realisations ("occurrence" or "non-occurrence"). In this section, we review the evidence in favour of the credit-to-GDP gap.Predicting banking crises is an exercise in compromise. The more familiar term, the credit-to-GDP ratio, is the ratio of a country's national debt to its gross domestic product. Country. They argue that their preferred alternative to the credit-to-GDP gap works better as an EWI for banking crises. Regardless of the classification approach for EMEs (purple and yellow lines), the AUC remains above the 0.5 threshold, although it is below the level that corresponds to the sample of advanced economies (blue line). site design / logo © 2020 Stack Exchange Inc; user contributions licensed under Reviewing the evidence, we show that the credit-to-GDP gap is on average (across many countries and several decades) the best This is a central feature of a CCB framework which combines rules and discretion. As we discuss below, it is the EWI of banking crises, having the best overall statistical performance among We use a panel of 26 countries over the period 1980-2012 to compare the performance of six indicators: the credit-to-GDP gap, credit growth, GDP growth, residential property price growth, the debt service ratio (DSR) and the non-core liability ratio.We follow Drehmann and Juselius (2014) in evaluating the forecast performance of EWIs using the area under the curve (AUC): a statistical methodology that captures the trade-off between true positives and false positives for the full range of policymakers' preferences (see The AUC (the area under the receiver operating characteristics curve, or ROC curve) is a statistical tool used in assessing the performance of signals that forecast binary events (ie events that either occur or do not). There are both statistical and economic counterarguments against this criticism.From a statistical point of view, the criticism regarding the correlation between the credit gap and real GDP growth is only partly correct. In addition, a valid rule of thumb suggests using the credit gap only for credit-to-GDP series with at least 10 years of available data.We argue that the conceptual criticism that the credit gap is not aligned with the buffer's objective misinterprets this objective and, by extension, the envisaged role of the guide. These preferences are shown as straight lines. I am wondering, what exactly does this mean? A simulation of this very dramatic swing (Lastly, another measurement issue is linked to routine statistical revisions in the underlying data. All known EWIs fall short of this ideal, and hence they must be evaluated on the basis of how they trade off the rate of missed crises against the rate of false positives (ie the percentage of signals they emit for crises that do not happen).
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