Remarks by Stephen S. Poloz
Governor of the Bank of Canada
Eric J. Hanson Memorial Lecture
University of Alberta
May 25, 2020
Edmonton, AB
Monetary policy in unknowable times
Introduction
It is an honour for me to deliver the Eric J. Hanson Memorial Lecture for 2020. I
want to thank the University of Alberta for the invitation and for your flexibility and
perseverance in these difficult and unusual times.
Eric Hanson was a great economist and Albertan whose pioneering work on
public finance helped define the Canada we know today. The lecture series
established in his memory has seen contributions from some very important
names in Canadian economics and public policy, such as Thomas Kierans,
Judith Maxwell and Kevin Lynch, to name just a few.
The Bank of Canada has been involved with this lecture series almost from the
beginning. I was working at the Bank in 1988 when then-Governor John Crow
delivered the second-ever Hanson Lecture. Crow cemented the idea that price
stability should be the prime mission for the central bank because it is the best
contribution monetary policy can make to our economic well-being. That lecture
laid the foundation for inflation targeting-the highly successful monetary policy
framework now practised in Canada and virtually every advanced economy.
David Dodge would follow John Crow 20 years later, at a time when the global
economy was poised on the edge of crisis. Dodge questioned whether inflation
targeting really did represent the end of monetary policy history, despite the Bank
of Canada's
success in meeting its objectives. As Dodge rightly pointed out, our
knowledge remains very limited, and monetary policy-makers should always
approach their task with humility and crossed fingers. Five years later, Mark
Carney would be the third Bank of Canada Governor to take this stage,
discussing the lessons policy-makers learned from the 2008-09 global financial
crisis-including the need to understand how monetary policy interacts with other
macroeconomic policies.
I am grateful for the opportunity to join this distinguished list. When the possibility
of delivering this lecture first arose last year, I knew that I wanted to talk about
uncertainty as it relates to the conduct of monetary policy. After all, the past
seven years have been marked by several major events that have forced
policymakers to confront unprecedented uncertainty-unprecedented in both
I would like to thank Sharon Kozicki and Paul Badertscher for their
invaluable collaboration in preparing this lecture.
Not for publication before May 25, 2020
13:30 Eastern Time
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magnitude and origin. Economists, including many at the Bank of Canada, have
responded by developing ways to think about and formally incorporate these
uncertainties into the conduct of monetary policy.
Given that I have been part of this history, I thought I could put the bow on my
time as Governor by going to Edmonton and talking to a knowledgeable and
engaged audience about uncertainty and monetary policy.
Then came the global tragedy we know as COVID-19. The clear imperatives for
authorities around the world were to flatten the curve of infections, try to keep
health-care systems from becoming overwhelmed and minimize preventable
deaths. This led to severe but necessary restrictions on the movement of people
and the shuttering of large sections of the economy. Policy-makers tried to
cushion the blow through economic policies of historic size and scope, put in
place for an undetermined length of time. Clearly, this is uncertainty in the
extreme.
Today, there are encouraging signs in Canada that efforts to flatten the pandemic
curve are paying off, and jurisdictions are taking tentative steps toward relaxing
containment measures. But it is safe to say that the policy-makers who will guide
us to whatever "normal" turns out to be will be dealing with
unparalleled
uncertainty. They will have to deal with all the unknowns surrounding the restart
of shuttered sectors, the reconstruction of broken value chains, the unwinding of
emergency measures and the unpredictable behaviour of consumers and
business leaders. Some of the financial vulnerabilities already present in the
economy will have grown worse, and other sources of vulnerability are likely to
emerge. We are truly entering unknowable times.
Obviously, the context has shifted dramatically since I first started thinking about
this lecture, but that has made the topic even more fitting. I will review how
monetary policy practitioners have dealt with uncertainty in the past and how that
has changed, particularly since the global financial crisis. Specifically, I will
illustrate how a risk management approach to monetary policy has been
developed over time, by describing case studies from my tenure at the Bank and
referring to supporting research along the way. Finally, I will offer some early
thoughts on how the lessons of the recent past will be applied as policy-makers
face massive uncertainty in the months and years ahead.
Dealing with uncertainty: an evolution
Uncertainty has always been a key part of practical economics and policy
making, and a large body of academic literature has developed over the years to
provide guidance on how to make policy under uncertainty. Much of the literature
builds on the thinking of American economist Frank Knight, who a century ago
came up with a way to classify the things economists don't know
about.1 Knight
used the word "risk" for situations where it is possible to calculate the probability
of various outcomes. The term "Knightian uncertainty" came to refer to situations
where calculating the odds is impossible. In reality, most situations fall
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somewhere between these two extremes, but the notion of Knightian uncertainty
remains pertinent today.
The means by which policy-makers account for and respond to uncertainty has
evolved over time, as have the types of uncertainty they have faced in making
decisions.2 Consider all the uncertainties involved in using economic models-
the indispensable tools for practitioners of monetary policy. The Bank of Canada
is an inflation-targeting central bank. We know that our policy actions can only
affect inflation in the future, after a lag that reflects all the complexity of the
monetary policy transmission process. This means it is crucial that we have a
deep understanding of the economy to guide our policy decisions. We have
developed increasingly sophisticated economic models to help inform our
decisions. Their predictive power comes from the various linkages between the
variables in those models. These interdependencies represent our best
understanding of the structure of the economy-a coherent view of how all the
pieces fit together.
While these interdependencies make the model a powerful tool for the
policymaker, they can also be its Achilles heel. This is because a systematic error in
just one component of the model can affect how it projects many variables.
Consequently, from the earliest use of forecast models, policy-makers have had
to take into account "model uncertainty," whether associated with parameters or
specification. Model parameters are not predetermined and may change over
time, which inserts uncertainty into the model outputs and implied policy
recommendations. Besides, uncertainty about model specification-which is to
say, the basic ingredients of the model-reflects the fact that there is much we
do not know about the fundamental structure of the economy.3 Simply put, all
models are simplifications of the real world. Because policy-makers today rely on
models to develop coherent economic forecasts and policy decisions, it follows
that a certain amount of uncertainty must be incorporated into the
decisionmaking process.
The possible sources of uncertainty do not stop there. Policy-makers must also
be aware of measurement error, which arises because many key concepts for
monetary policy-such as potential output-are unknown and must be estimated.
Measurement error also applies to the underlying data, which are subject to
revision.
A scan of the economics literature reveals several other forms of uncertainty that
are pertinent to the policy-maker. These include mandate uncertainty, which can
affect economic performance when consumers and businesses are unsure about
the central bank's policy
objectives. Mandate uncertainty has decreased in
Canada since the early 1990s, when the Bank and federal government first
spelled out an agreement on a framework for inflation targeting containing an
2
Mendes, Murchison and Wilkins summarize and compare the main results that have emerged in the
literature on optimal monetary policy under uncertainty with actual central bank behaviour in
R. R. Mendes, S. Murchison and C. A. Wilkins, "Monetary Policy Under Uncertainty: Practice Versus
Theory," Bank of Canada Staff Discussion Paper No. 2017-13 (November 2017).
3
An important aspect of model uncertainty relates to how households and firms in the model form
expectations.
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The statement of the central bank's policy
explicit objective for inflation.
framework in 1991 was followed by several moves to increase transparency.
These began under the leadership of Governor Gordon Thiessen and continued
through the work of Governors David Dodge and Mark Carney.4 As transparency
increased and the Bank developed a reputation for achieving its inflation targets,
we earned credibility and mandate uncertainty was essentially eliminated.
Since 1995, we have regularly published the Monetary Policy Report (MPR),
a thorough update of the Bank's economic outlook. Since 2009,
which provides
each MPR has had a separate section spelling out what we see as the main risks
to the outlook. Before 2000, interest rate announcements took place only when
the rate was changing, and you can imagine the anticipation and angst that was
often present in financial markets. Since 2000, some of that uncertainty has been
removed, as we have established fixed announcement dates (FADs) for our
policy interest rate announcements. At each FAD we issue a press release that
explains our economic outlook and any changes since the previous FAD,
identifies uncertainties clouding the outlook and reinforces market understanding
of the Bank's reaction Governing Council's
function. In 2013, we started including
assessment of the risks directly in the press release, in part to emphasize our
risk management process.
There is much more to the Bank's efforts to increase transparency,
so the topic
of communications will be an important thread in this lecture. For now, let me just
say that clear and transparent communications are vital for monetary policy. We
recognize that policy is more effective, and the economy works better, when the
central bank is clear about its target and the reaction function that helps it
achieve the target. Clear communication improves decision making throughout
the economy by reducing various sources of uncertainty, including mandate
uncertainty.
The usual practice for policy-makers is to see how well their models forecast
economic developments and figure out the cause of any errors. Often, the errors
can be considered benign white noise generated by various sources of
uncertainty. However, when the data consistently show results that are different
from what the model projects, it could be a sign that something more important is
at play. Because of all the interdependencies, we have to guard against the
possibility of persistent problems with the model, which can lead to a biased
projection and monetary policy errors.
The problem with this common practice was revealed during the global financial
crisis and subsequent Great Recession. The events illustrated a key weakness in
most macroeconomic policy models at the time. In particular, they captured fewer
and less-detailed linkages between financial markets and the real economy than
ideal. They also revealed the need for the profession to work toward the goal of
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having a grand, unified model that adequately captures both macroeconomic and
financial sector risks.5
In addition to illustrating the limitations of our models, the global financial crisis
and Great Recession heralded the start of a series of shocks that have emanated
from events beyond our borders and beyond our control. These kinds of shocks
often cannot be easily modelled-they are examples of Knightian uncertainty.
Today, the obvious example is the shock associated with the pandemic and the
global policy response. Before this year, I would have illustrated this type of
Knightian uncertainty by talking about global trade policy, as we had witnessed
the emergence of trade wars that threatened to change the global trading
system. The associated business uncertainty became a factor that on its own
weighed on economic growth by dampening business confidence, investment
and exports.6 Forecasting the size and duration of the impact of this uncertainty,
particularly given the on-again, off-again nature of trade talks and the
unpredictable nature of the protagonists, required an enormous amount of
judgment by policy-makers.
The development of a risk management approach
In 2013, when I returned to the Bank, it was obvious that the Canadian and
global economies were dealing with many uncertainties and risks that lay outside
the scope of our main monetary policy models of the time. This represented a
clear challenge for our monetary policy. During my first year as Governor, I
delivered a speech that spelled out the importance of risk management to
monetary policy.7 I also offered some early thoughts about risk management in a
paper I delivered to the Canadian Association for Business Economics in 2014.8
Moving monetary policy from the theoretical, or formulaic, space into a problem
of risk management acknowledges and accepts the uncertainties inherent in
5
An early contribution in this direction built additional financial system detail into a small open-economy
macroeconomic model and included various macroprudential policy levers. See S. Alpanda, G. Cateau and
C. Meh, "A Policy Model to Analyze Macroprudential Regulations and Monetary Policy," Canadian Journal
of Economics 51, no. 3 (August 2018): 828-863. Potential trade-offs faced by monetary policy were
addressed by M. Shukayev and A. Ueberfeldt, "Monetary Policy Trade-offs between Financial Stability and
Price Stability," Canadian Journal of Economics 51, no. 3 (August 2018): 901-945. In addition, Bank
research has examined the effectiveness of macroprudential and monetary policies to address
vulnerabilities. See, for example, S. Alpanda and S. Zubairy, "Addressing Household Indebtedness:
Monetary, Fiscal or Macroprudential Policy?" European Economic Review 92 (February 2017): 47-73.
6
Bank staff analyses that relate elevated uncertainty of various types to weaker economic activity include
S. Jo, "The Effects of Oil Price Uncertainty on Global Real Economic Activity," Journal of Money, Credit and
Banking 46, no. 6 (September 2014): 1113-1135; S. J. Byun and S. Jo, "Heterogeneity in the Dynamic
Effects of Uncertainty on Investment," Canadian Journal of Economics 51, no.1 (February 2018): 127-155;
L. Ferrara and P. Guérin, "What Are the Macroeconomic Effects of High-Frequency Uncertainty Shocks,"
Journal of Applied Econometrics 33, no.5 (August 2018): 662-679; R. Sekkel and S. Jo, "Macroeconomic
Uncertainty Through the Lens of Professional Forecasters," Journal of Business & Economic Statistics 37,
no. 3 (2017): 436-446.
7
S. S. Poloz, "Monetary Policy as Risk Management" (speech to the Canadian Club of Montréal, Montréal,
December 12, 2013).
8
S. S. Poloz, "Integrating Uncertainty and Monetary Policy-Making: A Practitioner's Perspective," Bank of
Canada Staff Discussion Paper No. 2014-6 (October 2014).
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policy making. This does not mean rejecting the use of models in decision
making. In fact, the Bank's various models provide the
base case that serves as
Governing Council's
the starting point for deliberations. They are also used to
simulate alternative scenarios, which is an excellent means of reaching a fuller
understanding of the risks we face.9 The essence of risk management is
identifying the most important risks and uncertainties around the outlook. We
examine the probabilities that the risks will be realized, consider alternative
futures related to uncertainties and think about the potential consequences of
making a policy error. We then choose a policy course that weighs these risks
and uncertainties in order to best manage them. This process can entail a degree
of flexibility around the inflation target itself, allowing inflation to return to target
more slowly or quickly than on average, while keeping in mind that our target sits
within a control range of 1-3 percent.
Given all the uncertainties and risks, it does not make sense to think a single,
optimal path for our policy interest rate will be consistent with achieving our
inflation target. It makes no sense to try to engineer such a path with precision.
Instead, we recognize that for every base-case economic projection, a wide
range of possible interest rate paths could ultimately be broadly consistent with
the inflation target. We weigh the various risks associated with things we do not
know and then take actions that are based on a nuanced balancing of those
risks.10 In doing so, we often use several smaller, specialized models to augment
the input from our main projection models, look at anecdotal and survey evidence
and apply considerable judgment.
Notice that balancing the risks we face also requires taking into account the
starting point for the economy, inflation and interest rates. Consider a situation
where inflation is below target, as was often the case in the wake of the Great
Recession. With the right monetary policy, an economic model will project a path
for inflation that gradually rises toward the target. A forecaster might look at the
projection and see that the risks to the inflation projection appear to be
statistically balanced. In other words, inflation is just as likely to end up below
that path as above it.
But the policy-maker may have a different view. If the starting point for inflation is
already below the target, any random shock that drives inflation even further
below target would be worse from a policy-maker's perspective than a shock that
pushes inflation up toward the target. So, even if statistical risks to the projected
inflation path are roughly balanced, the risks in terms of inflation outcomes may
be skewed in the policy-maker's eyes. And, the closer the policy rate is to its
effective lower bound, the more skewed this risk becomes. So, when the starting
9
Some of these alternative scenarios have been published in Bank of Canada staff analytical notes:
R. Barnett and R. Mendes, "A Structural Interpretation of the Recent Weakness in Business Investment,"
Bank of Canada Staff Analytical Note No. 2017-7 (July 2017); J. Yang, B. Tomlin and O. Gervais,
"Alternative Scenario to the October 2017 MPR Base-Case Projection: Higher Potential Growth," Bank of
Canada Staff Analytical Note No. 2017-18 (October 2017); K. B. Charbonneau, "The Impact of a Trade War:
Assessment of the Current Tariffs and Alternative Scenarios," Bank of Canada Staff Analytical Note No.
2019-20 (July 2019).
10
See the discussion of the Bank's risk management approach to monetary policy in Bank of Canada,
Renewal of the Inflation-Control Target: Background Information-October 2016 (Ottawa: Bank of
Canada, 2016).
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point for inflation is low, the policy-maker would be more concerned about
downside risk to inflation than upside risk and would rebalance the risks, keeping
monetary policy more stimulative than the model and the forecaster would
recommend.
The application of risk management becomes particularly important as a wider
array of risks enter into policy deliberations. In the aftermath of the global
financial crisis, the economy took a long time to return to full capacity and
inflation loitered below target. As a result, interest rates remained low for longer
than expected, driving an increase in borrowing that showed up in household and
housing sector vulnerabilities. Elevated financial vulnerabilities can change how
interest rates affect the economy, adding even more uncertainty into our
models.11 For instance, at high levels of household indebtedness, a reduction in
interest rates may not provide as much economic stimulus as when debt levels
are low. The main reason for this is that fewer households would likely be
interested or able to take on additional debt. In contrast, the restraining effect of
interest rate increases could be larger, both relative to history and relative to the
impact of a similar-sized interest rate reduction.12 This is because interest rate
changes affect disposable income, and highly leveraged borrowers might save a
large proportion of increases in disposable income but cut their consumption
steeply when their disposable income declines.13
Financial vulnerabilities are also important because they can affect the "time
trade-off" for economic growth. Specifically, a reduction in interest rates today will
boost near-term economic growth through greater borrowing. Greater borrowing
increases financial vulnerabilities, which in turn increase the risk that a future
negative shock will have a magnified effect on the economy. This leads to much
slower economic growth and makes the inflation target more difficult to achieve.
In short, by acting aggressively to achieve its inflation target today, the Bank can
make it harder to achieve its target in the future. The effect is to trade faster
growth today for slower growth in the future. The Bank is working on new tools to
We use models to capture the
help evaluate this time trade-off. "growth-at-risk"
downward asymmetry in the distribution of growth associated with elevated levels
of household indebtedness.14
All this to say that financial vulnerabilities and the risks they pose for future
growth and inflation have earned a place at the table as the Bank considers its
the Bank's inflation target
policy options. Let me emphasize, however, that
remains the overarching goal-the prime mission of our monetary policy.
11
See, for example, T. Duprey, "Asymmetric Risks to the Economic Outlook Arising from Financial System
Vulnerabilities," Bank of Canada Staff Analytical Note No. 2018-6 (March 2018).
12
The destabilizing effects of tightening in the short term are examined in G. H. Bauer and E. Granziera,
"Monetary Policy, Private Debt, and Financial Stability Risks," International Journal of Central Banking 13,
no. 3 (2017): 337-373.
13
In 2017, the Terms of Trade Economic Model (ToTEM) was revised to have improved modelling of
household debt and a more-detailed modelling of the housing market. See Bank of Canada, "Appendix:
Recent Changes to ToTEM," Monetary Policy Report (October 2017): 24-25.
14
For more details on growth at risk, see T. Duprey and A. Ueberfeldt, "How to Manage Macroeconomic
and Financial Stability Risks: A New Framework," Bank of Canada Staff Analytical Note No. 2018-11 (May
2018); and T. Duprey and A. Ueberfeldt, "Managing GDP Tail Risk," Bank of Canada Staff Working Paper
No. 2020-03 (January 2020).
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Financial stability issues are generally not a significant constraint on monetary
policy actions. However, our policy framework does give us flexibility in the time it
takes to get inflation back to target, which allows us to make tactical decisions to
avoid unintentionally making financial stability concerns worse.
This can all seem vague and judgmental to those who would prefer a more
formulaic approach to monetary policy: inflation rises, interest rates rise, inflation
falls. Unfortunately, the world is really not that simple. First, because monetary
policy acts with a lag, we are always aiming to achieve our inflation goal in the
future, offsetting invisible shocks that threaten to move us away from target.
Therefore, a perfectly executed monetary policy would see interest rates moving
in anticipation of the consequences of recent shocks and inflation remaining on
target the whole time. In effect, the more effective monetary policy is, the more
difficult it may be to explain. Second, weighing one uncertain, complex risk
against another is inherently judgmental and not amenable to mechanical or
Governing Council's
formulaic decision making. The consensus approach to
decision making, coupled with enhanced transparency around the judgments
made about the risks, shows its strength in this regard.
Before moving on, let me stress that taking a risk management approach to
monetary policy is not a uniquely Canadian practice. The Chair of the US Federal
Reserve, Jerome Powell, describes the Fed's approach to policy as having
"three
important parts: monitoring risks; balancing risks, both upside and downside; and
contingency planning for surprises."15 Possibly a better description of risk
"common sense since it reflects
management would be to call it a approach,"
reality rather than the elegance of economic theories.
Over the years, we at the Bank have increasingly viewed monetary policy
through a risk-management lens. And this has led to a wide variety of changes in
processes and procedures and has sparked many key avenues of research.
What I'd like to do now is go through
two case studies from the past seven years,
show how risk management applied to each situation and talk about changes in
our processes that emerged. Then, I will turn to monetary policy and the
unknowable implications of the pandemic.
Case study 1: the oil price shock of 2014-15
Let us look back at the oil price shock of 2014-15. In 2014, amid signs of
improving business investment and exports, real gross domestic product was
expanding at a rate above its estimated potential. Core inflation rose through the
year to around 2 percent. Following a prolonged period of low interest rates,
household spending was robust, and Canada continued to experience rising
prices in key housing markets and increasing risks from elevated levels of
household indebtedness.
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At the same time, an important upside risk was the possibility of a more robust
recovery in the United States. Finally, a key two-sided risk was the possibility of
greater-than-expected strength in housing and consumption, which would boost
Canadian economic growth while increasing the likelihood and potential severity
of a correction later on.
All of this presented a complex mix of risks to weigh. Through 2014, we judged
that the risks to our inflation projection were roughly balanced, while the financial
stability risks associated with household imbalances continued to edge higher.
Overall, the balance of risks was such that we felt the prevailing setting of the
policy interest rate was appropriate to keep inflation aimed at our
2 percent target.
But by January 2015, it became clear that low oil prices would endure, that the
energy industry would be making major markdowns to their investment plans and
that Canada would need to adjust to a significant drop in its terms of trade. The
decline posed a sufficiently large downside risk to our inflation outlook to warrant
a reduction in the policy interest rate. We cut rates again in July, bringing our
policy rate to 0.5 percent, as the persistence and impact of the shock became
clearer.
Note that throughout this episode, financial vulnerabilities associated with
elevated levels of household debt and rising house prices could have motivated
higher interest rates, while projections of declining inflation would have favoured
lower interest rates. In other words, financial stability risks were working at cross
purposes from macroeconomic risks; cutting interest rates would reduce
macroeconomic risks but increase financial stability risks at the same time. But
the oil price decline ultimately proved to be sufficiently large that it materially
shifted the balance of risks and led the Bank to lower the policy interest rate in
support of its primary mission.
Risk management considerations helped inform our decisions in several ways
throughout this episode. To begin with, Governing Council was receiving input
from multiple macroeconomic models, each with its own strengths and
perspectives. The Bank was using ToTEM-the Terms of Trade Economic
Model-a state-of-the-art dynamic stochastic general equilibrium model.16
ToTEM anticipated how serious the oil price shock would be, how the effects
would persist and how the economy would adjust to lower oil prices.17 But we
ToTEM's advice with
were able to cross-check LENS-the Large Empirical and
Semi-structural model.18
Using multiple models for projections is a hallmark of risk management. In fact,
the Bank was already using ToTEM and LENS by 2013 as we worked to
understand a series of surprisingly weak export data. The Bank also developed
other models for exports to inform that part of the projection.19 We always strive
for transparency about the development and use of our models. We summarize
new analysis in MPRs and publish related staff analytical notes, staff discussion
papers and technical reports.
Another feature of risk management I can highlight is the use of so-called soft
data to inform our process. Our decision to lower interest rates was influenced by
conversations with businesses in the oil sector. There would not be a lot of hard
data on Canadian investment intentions until well after the fact. But through our
Business Outlook Survey, as well as through personal conversations with oil
industry executives, we were able to estimate in a timely way the scale of future
reductions in business investment.20
The oil price shock also coincided with an important communications advance for
the Bank. I am referring to the opening statements that either the Governor or
Senior Deputy Governor give at the start of the press conference that follows
publication of an MPR.
The usual practice had been to use the opening statement simply to restate the
key points of the FAD press release and takeaways from the MPR, or even to
read the press release verbatim. By the time of the January 2015 MPR, we had
decided to make the opening statement a vehicle for elaborating on the key
issues in Governing Council's deliberations. The statement thus
serves the same
purpose as minutes of the deliberations of other central banks and does so in a
way that is more timely and often more concise and efficient.
While the change to the format of the opening statement was an advance in
transparency, our moves to lower interest rates in 2015 sparked some
complaints from financial market participants. They felt we had not been
sufficiently transparent with our intention to lower our policy rate before acting.
This reaction was seen by some as a natural consequence of our decision in
2013 to stop giving routine guidance about the future path of interest rates in our
To be clear, I'm
FAD press releases. referring to general statements about the
future direction of interest rates, not specific commitments about monetary policy
that form part of the Bank's policy tool
kit for extraordinary times.
Offering routine forward guidance about the future path of interest rates obviously
makes it easier for financial market participants to predict our policy. It can be
argued that it makes markets more efficient because it reduces uncertainty.
However, it does not reduce the total amount of uncertainty. Uncertainty is simply
shifted onto the central bank's shoulders.
When we decided to stop giving guidance, it was at a time when we could not
fully explain why exports and business investment were weaker than our
economic models were projecting.21 We wanted markets to appreciate the
uncertainty we were facing and were concerned that providing forward guidance
was giving participants a false sense of certainty. By being honest about the
extent of our uncertainty, and by not offering false certainty, we managed to shift
some of the uncertainty off our plate and put it back into markets.
This is a positive and important development because routine guidance also
comes with a cost-it suppresses the natural signalling role of financial markets.
When market participants pay attention only to the words we say about future
policy and begin to discount the flow of actual economic data, they set up a
oneway bet in markets.22 Market prices cease acting as a check for our own
projections. Bank research suggests that this can also lead to inefficient capital
flows.23
It is normal that there will be times when market views about the future of the
economy and interest rates, and those of the central bank, do no