Measuring the Effects of a Shock to Monetary Policy - Humboldt ...

Bayesian FAVARs with Agnostic Identification 5
sampling seems to be unfavorable.
The aim of this paper is threefold. First I critically examine the result by BBE and
provide an answer why they arrive at inferior results and how one should apply this pro-
cedure to receive more reasonable results. I like to combine the Bayesian FAVARs with
the agnostic identification by Uhlig (2005). To my best knowledge this has not been done
yet in the current literature on empirical macroeconomics. Hence I tackle the question
raised above in a complete consistent Bayesian framework. Not only the factors and the
parameters of interest are estimated with Bayesian methods, more importantly I apply
the agnostic identification in order to identify the specific effects induced by contrac-
tionary monetary policy. Here I try to be as precise as possible and as close as possible
to the conventional wisdom. This is accomplished by setting different ”block criteria”
that successively become stricter, while imposing more restrictions. This approach holds
an enticing promise in that one can narrow down the space of economically reasonable
dynamic reactions as accurate as the data allows. We do not have to set restrictions on
only one price variable, one of the monetary aggregates and one short term interest rate
as it has been applied so far and is common practice when applying sign-restrictions in
the VAR framework. Having available such a large datasets, provides the possibility to
be more strict in imposing e.g. not only the CPI to react non-positively after a monetary
policy shock but also other price variables that are incorporated in the estimation proce-
dure. This serves the possibility to identify the structurally, reaction of the economy in
an more exact manner than other identification approaches common in the literature on
empirical macroeconomics.
The second contribution and also the most time consuming one was to provide a Mat-
lab code that does the estimation and identification. Here the major challenge has been
to provide a code that is as efficient as possible, especially with respect to the calculating
time and the memory required in a way that also students can run the program on PCs of
”common” capacity without waiting a week for the results. The Gibbs sampling procedure