Measuring the credit gap: a forecast combination approach

Abstract

Abstract This paper proposes a new approach to calculating the credit gap: the deviation of the credit-to-GDP ratio from its long-run trend. Our method weights credit gap measures from different decomposition methods based on their out-of-sample forecasting performance. The results show that this weighted approach to estimating the credit gap outperforms other popular trend-cycle decomposition methods in predicting changes in the credit-to-GDP ratio. Furthermore, we also show that this combined credit gap measure can help mitigate the endpoint problem that is associated with conventional measures of credit gap.

Keywords

• Credit gap
• Trend-cycle decomposition
• Forecast combination
• Random forest model