FRBSF Economic Letter
2004-14; June 11, 2004
Policy Applications of a Global Macroeconomic Model
Central banks and other policy institutions have a long history
of using macroeconomic models to help prepare forecasts and to
quantify the economic consequences of various policies. Likewise,
private sector firms have long depended on models to summarize
these complex interactions succinctly and to evaluate the likelihood
of specific macroeconomic outcomes; this is especially true for
financial institutions, where such models can help with capital
investment and asset allocation decisions.
Typically, such macroeconomic models focus on a single country
and treat outcomes in the rest of the world as an input that is
not explicitly modeled. As international trade has burgeoned, however,
interest in models that formally account for global developments
has emerged. Although such "multi-country" models have
existed for some time, they are often large and complex and can
be cumbersome and unwieldy to employ, which limits their usefulness
in many situations.
Recognizing both the advantages that models can provide the private
sector and the difficulties with many existing multi-country models,
Pesaran et al. (2003) constructed a global macroeconomic model
that is both compact and relatively straightforward to implement.
Their global vector autoregression (GVAR) model combines national
and international variables using standard statistical methods
to forecast a core set of variables for multiple countries. The
authors motivate their model as a tool for valuing banks' global
asset portfolios and for quantifying the impact of global macroeconomic
shocks on the value of these portfolios.
In this Economic Letter, we summarize the key components of the
GVAR model and discuss its usefulness for monetary policy applications
and for credit risk management issues faced by financial institutions
and their government supervisors. We argue that while the GVAR
model is probably useful for credit risk management and could potentially
have some use for bank supervision, it is unlikely to be as useful
for monetary policy applications.
An overview of the GVAR model
The GVAR model that Pesaran et al. (2003) develop begins with
individual models of eleven countries and regions—the U.S., Germany,
Japan, China, France, the UK, Italy, Western Europe, Southeast
Asia, Latin America, and the Middle East—which collectively make
up about 80% of world GDP. The model for each country or region
seeks to explain six core macroeconomic variables: GDP, consumer
prices, nominal money supply, nominal equity prices, the nominal
exchange rate, and the nominal long-term interest rate. Focusing
on these broad aggregates rather than on disaggregate data, such
as personal consumption and business investment, keeps the model
small and manageable and helps make it easy to operate. The cost
of this broad aggregate focus, of course, is the inability to assess
the sectoral impact of shocks or to examine the aggregate effect
of sectoral shocks.
The GVAR model is pieced together by connecting these eleven
models, each of which is designed to mesh with the others, much
like the pieces of a jigsaw puzzle. Specifically, the six domestic
variables in each country/region are modeled, and their parameters
are estimated, using a particular form of vector autoregressive
model commonly used in macroeconomics. This form specifies that
the domestic variables depend on their lagged values and on current
and lagged values of "rest-of-the-world" measures of
the six variables. For example, in the Italian model, Italy's trade
weights with the other countries and regions are used to construct
the Italian measures of the six variables for the rest of the world;
the domestic variables in the Italian model then are taken to depend
on the current and lagged values of these trade-weighted aggregates
in addition to the lagged values of the domestic Italian variables.
To operate the aggregate model, one simply connects each of the
individual models by feeding into each the trade-weighted predictions
from the other ten models. The result is a system describing more
than 60 variables in terms of the lagged values of these variables,
which can be analyzed and used for forecasting without specialized
modeling software.
The GVAR model puts more emphasis on the time-series properties
of the macroeconomic data than on economic theory. In particular,
the model takes advantage of empirical estimates of the long-term
comovements between macroeconomic variables, known as cointegrating
relationships, to produce statistical summaries of the relationships
among the variables. Economic theory does guide the model's structure
to the extent that the "rest-of-the-world" variables
entering each country/region model are treated very differently
during estimation than the domestic variables, a version of the
small country assumption that makes sense from an economic standpoint.
More generally, though, the estimation is free to determine the
number and nature of any cointegrating relationships, and these
cointegrating relationships are not given an economic interpretation.
In order to use what is essentially a dynamic macroeconomic forecasting
model for credit risk analysis, a link is created by assuming that
the stock market value of a firm with outstanding debt is a function
of the regional and global macroeconomic environment in which it
operates. The link is a regression of firm stock returns on the
relevant domestic and international macroeconomic variables in
the GVAR model. If the value of the firm falls below a predefined
threshold, based on historical bond ratings, the firm is said to
default, and its debt is then worth a fraction of its face value.
By linking the GVAR model's macroeconomic output to the financial
health of firms in a bank's loan portfolio, the value of the portfolio
can be calculated for a specific set of macroeconomic outcomes,
or it can be simulated for a variety of outcomes.
Applications for macroeconomic
and monetary policy
Most central banks, including those in small open economies that
are highly exposed to external shocks, tend not to use global models
for policy formulation. The central banks in New Zealand, Australia,
and Canada all use models to some degree, but their models focus
on describing domestic variables, taking external factors as given.
By and large, central banks tend to concentrate their efforts on
using models for macroeconomic forecasting, and rather than forecasting
foreign GDP or foreign inflation themselves, they might use U.S.
Blue Chip macroeconomic forecasts as proxies. However, it is clear
that for policy simulations and scenario analyses, quantifying
and understanding the effects of global shocks requires a global
model.
Would the GVAR model be useful for policy formulation? Unfortunately,
the answer is likely to be no, partly because the model is not
sufficiently theoretical for policy analysis, and partly because
the limited dynamic structure used to describe each country or
region makes it difficult for the model to capture the mechanisms
at play in actual economies.
The importance of the latter point is made clear in Pagan (2003),
who used the Forecasting and Policy System (FPS) model, a small
open economy model of New Zealand operated by the Reserve Bank
of New Zealand, to show that simple time-series processes, such
as vector autoregressions, cannot easily represent accurately the
complex interactions at work in structural macroeconomic models.
Pagan's simulations indicate that higher-order lags of the macroeconomic
variables (on the order of ten) are needed to approximate the dynamics
of the FPS model, suggesting that the GVAR's simple dynamic structure
is likely to be insufficient.
The GVAR model has other drawbacks that make it less useful for
two kinds of analysis that are of particular significance to monetary
policymakers. The first kind of analysis involves issues related
to inflation stabilization. A model for this analysis must have
the property that inflation is not self-stabilizing, that policy
interventions are required to keep inflation low and stable. This
property is usually achieved by imposing restrictions on the dynamics
of inflation in the model, restrictions that are generally imposed
on models but are not imposed on the GVAR model.
The second kind of analysis is policy simulations. For policy
simulations, it is desirable to model the relationships between
the stocks and the flows of wealth, indebtedness, and capital stocks,
for example, in order to account for their effects on current outcomes
and in order to respect intertemporal budget constraints. This
matters particularly for policy simulations involving optimal policy
decisions where the absence of stock/flow relationships and intertemporal
budget constraints can appear to offer policymakers a free lunch.
The GVAR model does not allow for stock/flow relationships, which
limits its ability to address and answer many important policy
issues.
Applications for bank supervisory
policy
As the world has become more financially integrated, banks have
pursued loan opportunities outside of their home countries. Consequently,
the balance sheets of large banks typically contain assets that
span several countries.
To help ensure that banks hold sufficient capital reserves across
countries, the Basel Committee on Banking Supervision (BCBS) was
formed and, in 1988, it proposed an international standard of minimum
regulatory capital requirements that was set at 8% of risk-weighted
assets. More recently, the BCBS proposed the Basel II Accord to
make these capital requirements more sensitive to the underlying
credit risks in bank asset portfolios. The new Accord, which should
be completed by mid-year 2004 and fully implemented by year-end
2007, should hasten the efforts of banks and their supervisors
to examine credit risk and its underlying drivers, both macroeconomic
and others, more closely.
The GVAR modeling framework could be of use for banking supervision
in two ways. First, supervisors might use the GVAR model's ability
to examine several relevant macroeconomic series within and across
countries to detect increases in macroeconomic risks that could
affect bank portfolios. For example, a consistent forecast of the
consequences of a cyclical downturn could warn that the probabilities
of corporate defaults are increasing and hence that weaknesses
in the financial system are emerging.
However, several major implementation issues regarding the GVAR
model, as well as any related models, would need to be addressed
first. For example, the forecasting accuracy of the GVAR model
over time and with respect to different borrowers would need to
be tested thoroughly. It is interesting to note that in one empirical
example in Pesaran et al. (2003), the model is able to explain
only about a third of firm stock returns. Other important limitations
of the GVAR model are its emphasis on publicly traded firms (considering
that many bank borrowers do not have publicly traded equity), the
stability of the model's estimated coefficients over long time
periods, and the robustness of the model to structural shifts in
national and international economies. Finally, the benefits of
using an international model would be limited to banks that have
international lending exposures, which are typically an important
but relatively small subset of a national banking system. Also,
specific to U.S. supervisors, since the U.S. is essentially considered
a closed economy within the GVAR model, its usefulness for U.S.-based
institutions is further limited.
Regarding the Basel II process specifically, the GVAR model could
provide a convenient framework for approaching credit-risk stress-testing.
In Basel II, stress-testing refers to a bank's methodology for
analyzing the magnitude of credit losses that could arise under "stress" scenarios,
such as broad-based recessions, downturns in specific industries,
or large financial market movements. To date, the process banks
use to establish their stress-test scenarios and the methods they
use for stress testing contain more art than science. The GVAR
modeling framework could help credit managers improve these procedures
by providing a more coherent structure for considering the global
impact of shocks.
Supervisors could potentially benefit from the framework as well.
For example, supervisors might be able to use a GVAR model in fashioning
guidelines on how bank stress scenarios could be designed and in
reviewing the stress scenarios and testing procedures of specific
banks.
Conclusion
Macroeconometric models have been used historically by central
banks and other policy institutions. The work of Pesaran et al.
(2003) has extended these models to incorporate global macroeconomic
factors and introduced these models to the field of credit risk
management. In this Economic Letter, we focus on the GVAR model
and consider its possible application to public policy questions.
Regarding monetary policy, it is not clear that the GVAR model
would be useful for policy analysis. Aside from the fact that most
central banks do not typically model global economic factors, their
emphasis on policy simulations limits the GVAR model's usefulness.
Regarding credit risk applications, while the model's limited dynamic
structure is a concern, its ability to link specific firms' credit
quality to macroeconomic factors could make the GVAR model an interesting
alternative to the extant models. With respect to policy applications,
this characteristic could make it an attractive tool for supervisory
concerns regarding credit risk stress-testing.
Richard Dennis
Economist
Jose A. Lopez
Senior Economist
References
Pagan, A. 2003. "Reflections
on Some Aspects of Macro-Econometric Modelling." Invited lecture to
the African Econometrics Conference, Stellenbosch. http://www.sun.ac.za/internet/academic/economy/economics/conference/papers/paganafrecon.pdf
Pesaran, M.H., T. Schuermann, B.-J. Treutler,
and S.M. Weiner. 2003. "Macroeconomic Dynamics and Credit
Risk: A Global Perspective." Manuscript, University of Cambridge.
http://www.econ.cam.ac.uk/dae/repec/cam/pdf/cwpe0330.pdf
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