Monetary policy ; Inflation (Finance) ; Econometric models

Ben S. Bernanke

John B. Taylor

William Poole

English

Research Papers in Economics

  1 
Remarks for a Panel Discussion  
“Monetary Policy Modeling: Where Are We and Where Should We Be Going?” 
 
John B. Taylor 
Under Secretary of the Treasury for International Affairs 
 
At the Conference 
 
 “Models and Monetary Policy:  
Research in the Tradition of Dale Henderson, Richard Porter, and Peter Tinsley” 
 
Federal Reserve Board 
March 27, 2004 
 
 
It is a pleasure for me to participate in this conference honoring Dale Henderson, 
Richard Porter, and Peter Tinsley, three trail blazers in the field economic modeling and 
its application to economic policy.  I thank Jon Faust, Athansios Orphanides, and David 
Reisfschneider for organizing the conference and for inviting me.  
 
 
Monetary Policy Modeling: Where Are We? 
 
Today, stochastic simulation of econometric models and dynamic optimization 
with such models as a way to deal with real world uncertainty is a commonplace part of 
policy analysis.  It is hard to imagine, now, what it was like formulating monetary policy 
without such models.  Yet, back in the days before Peter Tinsley was directing the Fed’s  
“Special Studies” section, or before Dick Porter was directing the “Econometrics and 
Computer Applications” section, or before Dale Henderson was advising and leading 
countless young researchers in the Division of International Finance, that was what it was 
like.   Then it was only a dream that new quantitative methods could be applied in 
practice to improve monetary policy decisions.  Today it is a reality—a dream come 
true—thanks to the leadership of Dale, Dick, and Peter and to the many others they 
worked with inside and outside of the Fed.  
 
I believe the application of these methods in the area of monetary policy has made 
a real positive difference. It is a fact, of course, that macroeconomic performance has 
improved: the variability of real output is substantially lower, as are fluctuations of 
inflation, and inflation itself. There is a raging debate about the reasons for this 
improvement, but a more dynamic, quantitative, and systematic approach to monetary 
policy decisions in an explicitly uncertain environment must be given substantial credit.
1   
Interest rate decisions by the Fed and other central banks around the world have become 
more systematic, explicit, and responsive. This has been instrumental in helping push the 
world economy from the bad old days of high inflation and output instability. 
                                                 
1 Bernanke, Ben S. (2004). “The Great Moderation,” at the meetings of the Eastern Economic Association, 
Washington, D.C., February 20.   2 
 
The published research record of Dale, Dick, and Peter is substantial.  It 
demonstrates their originality and technical firepower, but it understates the role they 
played in pioneering the use of quantitative models in policy analysis.  Applying 
economic models in policy is not easy.  It requires much more than mathematical and 
statistical knowledge and experience with how the models work, though these of course 
are prerequisites.  It requires knowledge of the policy making process, including the 
politics and the personalities.  It requires good judgment, for example, about when model 
complexity or elegance must be sacrificed to create a more practical and constructive 
policy framework. It requires leadership and management skills to get the best team; this 
includes recruiting, retaining, and motivating.  
 
I have been fascinated and have written about this nexus between basic research 
and its application to policy, using the word “translational economics” which I borrowed 
from the work on “translational biology,” the study of the interface between biological 
research and its application to the medical fields. As I have said, “There are plenty of 
good economic ideas ‘out there on the shelf’ that do not affect practical decision making. 
Some direct action, perhaps by those working in the policy arena or perhaps by people 
close to policy making, are needed to take the academic research and to mold it into 
something useful to policy makers.” 
2  Dale, Dick, and Peter are masters of translational 
economics as well as basic economic research. 
 
 
And Where Should We Be Going? 
 
I think it would be most constructive if I approached this question by focusing on 
how we are using, and are planning to use, quantitative modeling to improve policy 
decisions in the areas of international monetary and financial policy at the U.S. Treasury. 
In fact, there are many common issues that arise both when applying quantitative 
methods to monetary policy and when applying quantitative methods to other areas of 
economic policy. 
 
  First, let me note that, in order to place greater emphasis on quantitative 
modeling—and here I include basic empirical work and the development of dynamic 
models dealing with uncertainty—three years ago we created a new section in 
International Affairs at Treasury.  It is called Quantitative Policy Analysis.  Like so many 
things in government it is now referred to by its acronym, QPA. It is a “cross cutting” 
section, interacting with the country desks and international financial institution offices of 
Treasury.  
 
Second, I am grateful that we at Treasury have been able benefit from the 
quantitative modeling capability at the Fed.  For example, we have benefited from work 
on the Federal Reserve Board staff’s new computable general equilibrium model 
                                                 
2 Taylor, John B. (1998). “Applying Academic Research on Monetary Policy Rules: An Exercise in 
Translational Economics,” Harry G. Johnson lecture, The Manchester School, Vol. 66, Supplement 1, pp. 
1-16.    3 
(SIGMA) to backup our discussions of the current account and supply side policies in 
recent meetings of Working Party Three of the OECD.  New models like this one have 
the potential to be the workhorses of the new “Agenda for Growth” in which structural or 
supply-side policies are the main focus, much as earlier models have been the workhorses 
of policy in the monetary field. They are examples, in my view, of where we should be 
going.  
 
Let me now briefly describe three specific policy applications that have developed 
as part of the work of the Office of Quantitative Policy Analysis.  They are also examples 
of where we should be going.  
 
 
Millennium Challenge Account: Quantitative Indicators of Pro-Growth Policies 
 
It is a basic principle of economics that reducing poverty in poor countries 
requires much higher economic growth. Countries are poor because productivity is low. 
To reduce poverty, poor countries must have much higher productivity growth rates.  The 
academic, empirical growth literature (and much experience) has shown that good 
economic policy has much to do with increasing economic growth.  In order to raise 
economic growth in poor countries, therefore, President Bush proposed a new 
Millennium Challenge Account (MCA), which encourages pro-growth policies in poor 
countries by allocating more aid to countries that are actually following good pro-growth 
policies.  
 
Making the MCA operational has required a quantitative approach.  In order to 
determine which countries would qualify for assistance, we had to find objective 
indicators of pro-growth policies.   The methodology evaluates three broad categories of 
policy performance 1) governing justly, 2) investing in people, and 3) encouraging 
economic freedom.  We then selected 16 quantitative indicators in these categories based 
on their relationship to economic growth.  We tried to keep the number of indicators 
small and make sure they were available for a large number of countries. We consulted 
with many people both inside and outside government in deciding on the indicators We 
developed a robust procedure for combining the 16 indicators: put simply, a particular 
candidate country must perform above the median on at least half of the indicators in 
each of the three policy categories.   
 
Ultimately, a board consisting of the Secretary of State, the Secretary of the 
Treasury, and others will determine funding allocations for the poor countries.  They will 
use their judgment, but their decisions will be based heavily on these quantitative factors. 
I think it is clear that this application of quantitative methods to policy decision has 
important similarities to the way that quantitative decision-making has been adopted in 
other areas of policy, including monetary policy.    
 
   .    4 
 
The Quantitative Impact of Changes in Fundamentals on Interest Rate Spreads 
 
Crisis prevention in emerging markets represents another area where quantitative 
modeling plays an integral role in policy formulation. In the last few years, the Treasury 
has developed a policy indicator model, called the “Blue Chip,” to help analyze 
developments in various emerging economies.  The model represents a management tool 
to systematically evaluate the risk of crisis. The Blue Chip scores serve as a rule to 
evaluate vulnerabilities by providing empirical signals of the level of relative risks, as 
well as shifts in those risks.  One policy response could be engagement with countries to 
encourage better policies and address potential vulnerabilities. 
 
The Blue Chip index is constructed from five separate risk assessments from five 
different institutions.  The index considers much of the recent research on early warning 
systems, as well as the research on sovereign crises developed over the last twenty-years.  
The Blue Chip index incorporates key variables such as the real exchange rate, domestic 
credit growth, M2/reserves, gross external financing requirements, global liquidity, as 
well as other measures.  We use percentile rankings to standardize scores across the five 
separate risk assessments and create a unified score for individual countries and emerging 
markets overall.  As with all quantitative indicators, discretion plays an essential role in 
the interpretation of the scores. The Blue Chip scores are evaluated by those Treasury 
staff who are most familiar with the countries and with various nuances influencing 
vulnerabilities.   
 
Figure 1 shows the time-series pattern of this Blue Chip indicator during the past 
two years.  Note that the Blue Chip has tracked the broad movements in the emerging 
market spread fairly well. (The EMBI spread is shown in the figure in standard deviation 
units.) Granger-causality tests suggest that the Blue Chip has use as a leading indicator.  
 
To the extent that the indicator is based on policy fundamentals, it gives an 
estimate of the amount by which the large recent decline in emerging market spreads is 
due fundamentals. Many analysts have been concerned in recent months that there has 
been overshooting of the spreads. Figure 1 does indicate some overshooting, but it is 
small relative to the decline in fundamentals as measure by the Blue Chip indicator.  
 
 
Changes in Emerging Market Contagion  
 
A third example relates to the problem of contagion in emerging markets, in 
which a financial crisis in one part of the world could spread to other parts of the world. 
Because contagion was such a large factor in the official sector policy responses to the 
financial crises in the 1990s, we have paid particular attention to it.  We have applied 
existing empirical work and have performed additional work when needed.   
 
Our research reviews and work showed that much contagion was due to the 
interconnections between counties, and would not be automatic when countries are not   5 
substantially interconnected.  Moreover, if policy changes could be better anticipated and 
policies in different countries could be differentiated, the amount of contagion would 
decline. In fact we detected a decline in contagion starting in 2001 period, even before 
the crisis in Argentina.  
 
For this reason we began to speak out about the decline in contagion in the spring 
and summer of 2001. We also tried to communicate better about our views on contagion 
and give the markets a clearer sense of our policy intentions, trying to minimize the 
surprise element as much as possible.  
  
  Figure 2 gives a simple graphic representation of the way in which contagion was 
so different following the Argentine default in 2001 compared with the Russian default in 
1998.  As our empirical work began demonstrating in early 2001 and as our policy 
approach began reflecting this, there was a marked decline in contagion. The impact of 
Russian default in other parts of the world was far worse than the impact of Argentina in 
other parts of the world. 
 
Conclusion 
 
The success of quantitative modeling in the field of monetary policy is clear, and 
this is due in large part to the “translational economic” work by Dale Henderson, Richard 
Porter, and Peter Tinsley.  And, as the examples I have given here indicate, there is much 
potential in the further application of quantitative methods to policy.  I could only touch 
on a small number of issues we are working on at Treasury.  I would like to talk more 
about other applications, such as debt sustainability models or the development of a 
monetary framework for Iraq. It is clear that much remains to be accomplished in the area 
of applying economic models to practical policy problems.  I thank the organizers of this 
conference for promoting the discussion. 
 
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 Figure 2: Sovereign Risk Spreads After the Russian and Argentine Defaults 
 
 
Asia EMBI+ (1998-1999)
0
300
600
900
Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99
S
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Russian Default
(August 1998)
 
 
 
 
Africa EMBI+ (1998-1999)
0
300
600
900
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Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99
S
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Russian Default
(August 1998)
 
 
 
 
Latin America EMBI+ (1998-1999)
0
300
600
900
1200
1500
Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99
S
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Russian Default
(August 1998)
 
 
 
 
 
 
Asia EMBI+ (2001-2002)
0
300
600
900
May-01 Aug-01 Nov-01 Feb-02 May-02 Aug-02
S
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Argentine Default
(December 2001)
 
 
 
 
Africa EMBI+ (2001-2002)
0
300
600
900
1200
1500
1800
May-01 Aug-01 Nov-01 Feb-02 May-02 Aug-02
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Argentine Default
(December 2001)
 
 
 
 
Europe EMBI+ (2001-2002)
0
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May-01 Aug-01 Nov-01 Feb-02 May-02 Aug-02
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Argentine Default
(December 2001)
 
 
 
 

Panel discussion monetary policy modeling: where are we and where should we be going?

http://www.federalreserve.gov/events/conferences/mmp2004/pdf/taylor.pdf

Panel discussion monetary policy modeling: where are we and where should we be going?

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Research Papers in Economics