Unobserved components model estimates of credit cycles: Tests and predictions
估计了包含实际和金融趋势以及商业和信贷周期的未观测成分模型，发现金融趋势的斜率比估计的周期和巴塞尔缺口更能预测美国信贷与GDP比率，提示政策制定者评估金融稳定时应考虑金融部门的永久冲击。
This paper estimates unobserved components (UC) models with real and financial trends and business and credit cycles to assess different measures of the credit cycle used by policymakers. The permanent components of the real and financial sectors are a Beveridge–Nelson and local linear trend, respectively. The business and credit cycles evolve jointly as a second-order vector autoregression . Bootstrap methods are applied to UC model estimates retrieved from classical optimization of the predictive likelihood of the Kalman filter . Results indicate the slope of the financial trend better predicts the credit to GDP ratio in the United States than the estimated business and credit cycles and the Basel gap. This suggests policymakers should consider permanent shocks to the financial sector when gauging the state of financial stability .