Credit cycle fluctuations measurement in the context of pandemic shock.

This paper proposes and to empirically tests an alternative method of measuring credit cycle fluctuations with a "nominalized" state of the population used as a denominator to the sum of the credit in the economy. It is a response to the often-quoted disadvantage of the baseline approach that estimates the credit gap with HP-filtered credit-to-GDP time, which distorts information on the state of the credit cycle in periods of significant declines in nominal GDP. The COVID-19 pandemic caused an economic shock that created ideal conditions for another practical manifestation of this weakness almost on a global scale. In the proposed alternative method, the cyclical component from the adjusted credit per capita time series is obtained through HP filter, i.e., similarly as in the baseline approach. Although the "credit per capita" approach is not completely new in this research subject, we use some important innovative features, such as quarterly state of population and its "nominalisation" with the use of a GDP deflator. Our empirical results show that the proposed credit per capita approach proved to be more appropriate compared to the baseline credit-to-GDP approach, at least in periods of large swings in economic activity. This feature of the proposed innovative approach can be valuable in the sense of eliminating false signals to countercyclical regulation and assessment of a country's competitiveness to enhance the credibility and validity of the findings, adding value to the overall research outcome using data reliability validation. [ABSTRACT FROM AUTHOR]