Structural vector autoregressions (VARs) are widely used to trace
out the effect of monetary policy innovations on the economy.
However, the sparse information sets typically used in these
empirical models lead to at least two potential problems with the
results. First, to the extent that central banks and the private
sector have information not reflected in the VAR, the measurement
of policy innovations is likely to be contaminated. A second
problem is that impulse responses can be observed only for the
included variables, which generally constitute only a small subset
of the variables that the researcher and policymaker care about. In
this paper we investigate one potential solution to this limited
information problem, which combines the standard structural VAR
analysis with recent developments in factor analysis for large data
sets. We find that the information that our factor-augmented VAR
(FAVAR) methodology exploits is indeed important to properly
identify the monetary transmission mechanism. Overall, our results
provide a comprehensive and coherent picture of the effect of
monetary policy on the economy.
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