Factor-augmented VARs (FAVARs) have combined standard VARs with factor analysis to exploit large data sets in the study of monetary policy. FAVARs enjoy a number of advantages over VARs: they allow a better identification of the monetary policy shock; they avoid the use of a single variable to proxy theoretical constructs; they allow researchers to compute impulse responses for hundreds of variables. Their shortcoming, however, is that the factors are not identified and lack an economic interpretation.This paper seeks to provide an interpretation to the factors. We propose a novel Structural Factor-Augmented VAR (SFAVAR) model, where the factors have a clear meaning: Real Activity factor, Inflation factor, Financial Market factor, Credit factor, Expectations factor, and so forth. The paper employs a Bayesian approach to jointly estimate the factors and the dynamic model. This framework is then used to study the effects of monetary policy on a wide range of macroeconomic variables.
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