MONETARY POLICY WITH A WIDER INFORMATION SET: A BAYESIAN MODEL AVERAGING APPROACH
| Author | Fabio Milani |
| Published date | 01 February 2008 |
| DOI | http://doi.org/10.1111/j.1467-9485.2008.00446.x |
| Date | 01 February 2008 |
MONETARY POLICY WITH A WIDER
INFORMATION SET: A BAYESIAN
MODEL AVERAGING APPROACH
Fabio Milani
n
Abstract
Monetary policy has been usually analyzed in the context of small macroeconomic
models where central banks are allowed to exploit a limited amount of information.
Under these frameworks, researchers typically derive the optimality of aggressive
monetary rules, contrasting with the observed policy conservatism and interest rate
smoothing. This paper allows the central bank to exploit a wider information set,
while taking into account the associated model uncertainty, by employing Bayesian
model averaging with Markov chain model composition. In this enriched
environment, we derive the optimality of smoother and more cautious policy rates,
together with clear gains in macroeconomic efficiency.
I Intro ductio n
Monetary policy is usually studied in the context of small macroeconomic
models in which the central bank is implicitly allowed to exploit only a limited
amount of information. Most studies, in fact, assume backward- or forward-
looking models, which follow those proposed by Rudebusch and Svensson
(2002), McCallum and Nelson (1999), Clarida et al. (2000), and Woodford
(2003b), and which are typically characterized by only three economic variables:
inflation, a measure of the output gap, and a short-term interest rate.
But in reality, central bankers need to monitor a wide variety of economic
data and indicators. Monetary policy makers not only focus on current and past
values of the target variables, but they also analyze a large number of
intermediate targets and leading indicators, which are correlated with the actual
target variables, but they are more easily and promptly observable.
Motivated by this observation, this paper tries to incorporate a larger
information set in a simple empirical model of monetary policy. In the recent
literature, Bernanke and Boivin (2003) have provided a first example of a study
of monetary policy in what they label a ‘data-rich’ environment, but mainly with
emphasis on the effects of monetary policy shocks.
n
University of California, Irvine
Scottish Journal of Political Economy, Vol. 55, No. 1, February 2008
r2008 The Author
Journal compilation r2008 Scottish Economic Society. Published by Blackwell Publishing Ltd,
9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main St, Malden, MA, 02148, USA
1
The current paper aims, instead, to study how the expansion of the central
bank’s information set affects the choice of optimal monetary policy, comparing
the results with those typically obtained in more conventional environments. In
particular, we try to verify whether the incorporation of an enlarged information
set, together with the associated model uncertainty, might represent a solution to
an important unresolved issue in the monetary policy literature: the reconcilia-
tion with optimizing behavior of real-world central bank’s conservatism and
interest rate smoothing. In the context of small macroeconomic models, in fact,
it is common to derive the optimality of an excessively aggressive and volatile
monetary policy rule, which is at odds with the historically observed one.
This puzzle has lead to the development of an active stream of research. Sack
and Wieland (2000) survey some of the potential explanations for interest rate
smoothing offered in the literature, which consist of:
1. Forward-looking expectations: As Woodford (2003a) has argued, in the
presence of forward-looking market participants, policy rules characterized
by partial adjustment will be more effective in stabilizing output and
inflation, because a small initial policy move in one direction will be
expected to be followed by additional subsequent moves in the same
direction. This induces a change in future expectations without requiring a
large initial move. Castelnuovo (2006) empirically analyzes this argument.
2. Data uncertainty (real-time data): If macroeconomic variables aremeasured
with error, the central bank moderates its response to initial data releases in
order to avoid unnecessary fluctuations in the target variables. An example
of monetary policy using real-time data is Orphanides (2001).
3. Parameter uncertainty: If there is uncertainty about the parameters of the
model, an attenuated response to shocks would be optimal, as shown in the
original paper by Brainard (1967). Several recent papers have reinvestigated
this prediction (Sack, 2000 and Soderstrom, 2002 provide empirical examples).
None of these explanations, however, has been found to be entirely
convincing from an empirical point of view.
Rudebusch (2002), on the other hand, views interest rate smoothing as an
illusion, citing as evidence the unpredictability of the term structure, which is not
consistent with the large estimated smoothing coefficient. The papers by
Gerlach-Kristen (2004), English et al. (2003), and Castelnuovo (2003, 2007)
provide further tests of this view.
The current paper explores whether adding a richer information set and
accounting for the associated model uncertainty can justify the optimality of the
observed gradualism and smoothness. In our environment, the central bank takes
into account a variety of other data, in addition to inflation, output gap, and the
federal funds rate. As we focus on the United States, the additional variables included
in the model are some selected leading indicators that are recognized as important in
formulating monetary policy by the Fed and published in the NY Fed’s website.
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1
These selected indicators are published at http://www.ny.frb.org/education/bythe.html, and
described in more detail in the next section and in Appendix A.
FABIO MILANI2
r2008 The Author
Journal compilation r2008 Scottish Economic Society
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