Factor augmented vector-autoregression with narrative identification: An application to monetary policy in the US - Giorgia De Nora (Queen Mary University of London, School of Economics and Finance, 2021)
I extend the Bayesian Factor-Augmented Vector Autoregressive model (FAVAR) to incorporate an identification scheme based on an exogenous variable approach. A Gibbs sampling algorithm is provided to estimate the posterior distributions of the models parameters. I estimate the effects of a monetary policy shock in the United States using the proposed algorithm, and find that an increase in the Federal Fund Rate has contractionary effects on both the real and financial sides of the economy. Furthermore, the paper suggests that data-rich models play an important role in mitigating price and real economic puzzles in the estimated impulse responses as well as the discrepancies among the impulse responses obtained with different monetary policy instruments.
Keywords: 330; information sufficiency; factor-augmented VARs; instrumental variables; monetary policy; structural VARs