"Unobserved Components Model Estimates of Credit Cycles: Tests and Predictions"
Journal of Financial Stability (2023) [Link]
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 focus on permanent shocks to the financial sector to gauge the state financial stability.
"A New Keynesian Unobserved Components Model" (Job Market Paper) [Link]
Using an unobserved components (UC) approach, this paper estimates new Keynesian (NK) models which allow inflation to be nonstationary. My NK-UC models incorporate the Fisher equation into an otherwise standard three equation NK framework, forcing the nominal policy rate and inflation to share a common random walk trend. I estimate the NK-UC models on a quarterly U.S. sample of consumption, income, the nominal policy rate, and inflation from 1960 to 2018. The estimated NK Phillips curve (PC) shows that inflation responds more strongly to the output gap than the inflation gap. This result holds under various restrictions imposed on the NK-UC models including with and without habit formation in consumption, a hybrid-NKPC, and markup shocks.