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 .