Assessing the Transmission of Monetary Policy Using Time‐varying Parameter Dynamic Factor Models*
| Date | 01 April 2013 |
| Published date | 01 April 2013 |
| DOI | http://doi.org/10.1111/j.1468-0084.2011.00687.x |
| Author | Dimitris Korobilis |
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OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 75, 2 (2013) 0305-9049
doi: 10.1111/j.1468-0084.2011.00687.x
Assessing the Transmission of Monetary Policy Using
Time-varying Parameter Dynamic Factor Models*
Dimitris Korobilis†
†CORE, Université Catholique de Louvain, 34 Voie du Roman Pays, 1348,
Louvain-la Neuve, Belgium (e-mail: dimitrios.korompilis@uclouvain.be)
Abstract
This article extends the current literature which questions the stability of the monetary
transmission mechanism, by proposing a factor-augmented vector autoregressive (VAR)
model with time-varying coefficients and stochastic volatility. The VAR coefficients and
error covariances may change gradually in every period or be subject to abrupt breaks.
The model is applied to 143 post-World War II quarterly variables fully describing the US
economy. I show that both endogenous and exogenous shocks to the US economy resulted
in the high inflation volatility during the 1970s and early 1980s. The time-varying factor
augmented VAR produces impulse responses of inflation which significantly reduce the
price puzzle. Impulse responses of other indicators of the economy show that the most
notable changes in the transmission of unanticipated monetary policy shocks occurred for
gross domestic product, investment, exchange rates and money.
I. Introduction
A challenge of great importance in modern macroeconomics is to identify the contribu-
tion of monetary policy shocks to the economy over time. Over the course of the last
40 years the US economy has been characterized by transitory exogenous shocks, and
more pervasive events such as the liberalization of financial markets, and the decline in
output volatility and inflation persistence since the early 1980s (e.g. Kim and Nelson,
1999; McConnell and Perez-Quiros, 2000; Stock and Watson,2002). At the same time, the
conduct of monetary policy has also changed, with maintaining price and output stability
being the dominant strategy by the Fed. Since both monetary policy and the nature of
exogenous shocks have evolved dramatically, there is an obvious empirical challenge in
identifying the actual role of monetary policy actions in influencing observed changes in
the economy. It is not surprising that currently there is a vast empirical literature measuring
the monetary transmission mechanism with contradicting results. For instance, Boivin and
ÅThe author is grateful to John Geweke, Gary Koop, John Maheu, Simon Potter for helpful discussions,
and seminar participants at the Rimini Center for Economic Analysis, Banca d’Italia and Universit´e Catholique
Louvain for helpful discussions and comments. Comments from the Editor and two anonymous referees have helped
to substantially improve this article, for which I am grateful.
158 Bulletin
Giannoni (2006b), Cogley and Sargent (2001, 2005) and Clarida, Gal´ıand Gertler (2000)
argue in favour of a ‘good policy’ scenario, where monetary policy since the early 1980s
became more aggressive in stabilizing shocks to prices and aggregate activity. Primiceri
(2005), Sims and Zha (2006), Koop, Leon-Gonzales and Strachan (2009) and Canova and
Gambetti (2009) follow the traditional vector autoregressive (VAR) approach, formulated
econometrically to allow for the parameters to drift over time, and end up with mixed
results as to whether it is the shock or the propagation mechanism which has changed over
time; Giannone, Lenza and Reichlin (2008a) offer a detailed summary of this literature.
Common place in these studies is an attempt to measure the effects of monetary pol-
icy in the economy as a whole by using only a restricted set of variables, as implied
by New-Keynesian Dynamic Stochastic General Equilibrium (DSGE) models with three
endogenous variables describing economic activity, aggregate prices and monetary pol-
icy. Stock and Watson (2005) and Bernanke, Boivin and Eliasz (2005) point out that
when extracting the structural shocks from the innovations of a VAR it is important
to make sure that there is no omitted variable bias. Since during the decision process
there are hundreds of variables available to economic agents and policy makers, espe-
cially Central Banks (Bernanke and Boivin, 2003), it is expected that the innovations
of a VAR with just three variables will not span the space of structural disturbances.
This lack of information has also been identified as the source of the price puzzle –
the fact that prices increase following a contractionary monetary policy. In light of this
puzzle many authors, including Boivin and Giannoni (2006b), reformulate their three-
variable VAR by introducing a price index as an additional variable without success. In
fact Castelnuovo and Surico (2010) show that including a measure of inflation expectations
in the VAR is the way to correct the prize puzzle and they provide extensive simulations
to support this finding. Nowadays it is recognized that adding more and more information
to a VAR has the potential to resolve many anomalies observed empirically.
Dynamic factor analysis in the form described, for instance, in Stock and Watson
(2005) can do exactly this without introducing a degrees of freedom problem. In essence,
the Dynamic Factor Model is a means of summarizing information in a large data-set – in
the order of some hundreds of variables – using just a few (usually <10) latent variables
called factors. These factors can just be the first few principal components of the large
dataset, but also different methods for estimating latent factors have been proposed over
the course of the last years. Among the vast literature, notable recent studies include Boivin
and Ng (2005), Giannone, Reichlin and Small (2008b) and Boivin and Giannoni (2006a).
The recent implementations of Stock and Watson (2005) and Bernanke et al. (2005) have
the advantage of treating the dynamic factor model as a direct generalization of structural
VARs.
This study adopts a structural VAR framework combined with factors as the starting
point. Then, for the purpose of modelling the evolution of monetary policy in the US, the
parameters of the VAR are allowed to evolve over time. This assumption implies that the
transmission of monetary and non-monetary shocks can be measured over different points
in time. Subsequently, this study goes one step further from the standard dynamic factor
literature to identifiy the merits of using the currently popular time-varying parameters
VARs. That way, large datasets can be used in a model which allows both frequent and
infrequent breaks and adapts immediately to changes in regimes. Specifically for the US,
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