Christopher Kent: Economic forecasting at the Reserve Bank of Australia
Address by Mr Christopher Kent, Assistant Governor (Economic) of the Reserve Bank of
Australia, to the Economic Society of Australia (Hobart), University of Tasmania, Hobart,
6 April 2016.
* * *
Accompanying charts can be found at the end of the speech
I thank Daniel Rees, who provided invaluable assistance in preparing these remarks, and is the Head of the Bank's
new Macroeconomic Modelling Section (see below).
Introduction
Let me start by thanking the University of Tasmania and the Tasmanian branch of the
Economic Society of Australia for hosting this event.
Today I am going to talk about economic forecasting, which plays an important role in policy
deliberations at the Reserve Bank of Australia (RBA). It assists in interpreting economic
developments and, because monetary policy typically affects economic activity and inflation
with a lag, it is a necessary part of determining and communicating the appropriate stance of
policy.
I'm also going to discuss the results of an external review of our forecasting methods and
processes, which we have just published, and our responses to the recommendations therein.
Our current approach to forecasting has been in place for some years. While it has served us
well, we thought it was time to consider whether our methods and processes would continue
to be appropriate and how we might improve upon them. To do this, we commissioned two
eminent economists to conduct a review, Professor Adrian Pagan of the University of Sydney
and Dr David Wilcox of the Federal Reserve Board of Governors.1
The existing approach to forecasting at the RBA
The process of economic forecasting actually involves a wide range of activities. The most
familiar relates to the construction of forecasts that reflect our best estimates of future
economic outcomes.2 Each quarter in the Statement on Monetary Policy, we publish forecasts
for Australia's major trading partners' GDP growth, as well as Australia's terms of trade, GDP
growth, unemployment rate and inflation over the next two-and-a-half years. However, these
numbers are just a summary of an extensive process in which our analysts incorporate
incoming data, assess the relevance of new events, construct forecasts for a large number of
economic variables, analyse various scenarios and consider a range of key risks to the outlook.
All of this provides a useful framework to guide policy deliberations and then communicate
those decisions.
This process may lead us to revise the economic outlook - either in response to unexpected
developments or shocks, or to news that changes our views on how the economy is evolving.
If a change in the outlook is judged to be significant, it may warrant a change in the stance of
monetary policy. However, that link between the forecasts and policy is by no means a
mechanical one. In part, that is because any policy response will depend on the nature of the
change to the outlook, including whether it has been driven primarily by supply or demand
shocks, and how persistent those shocks are likely to be. Also, any policy response will depend
on an assessment of the extent to which the economy will adjust of its own accord. It is
important to recognise that sufficiently flexible economies can do much to right themselves,
with households and businesses responding to the signals provided by changes in prices,
wages and the exchange rate. Such changes have played a major role in the adjustment of
the Australian economy to the decline in commodity prices and mining investment over recent
years.3 Another important consideration is the sizeable degree of uncertainty about the
outlook. While a revision to the central outlook might appear to be of substance, it may still be
relatively small compared with the considerable degree of forecast uncertainty. And finally,
other considerations, beyond the near-term outlook of the macroeconomy, may be relevant,
including the prospects for financial stability.
The role of models
Modelling can play a useful role in the forecasting process, including by helping to identify the
nature of the shocks affecting the economy. Models can also provide a sense of how the
economy might respond to alternative policy paths or different assumptions about key
variables, such as commodity prices or the exchange rate to name just two. In addition, models
allow us to check whether our forecasts are consistent. For example, does the path for output
and employment imply a plausible path for productivity? And do they imply patterns of
economic behaviour that are broadly in line with historical experience? A recent instance is the
decline in wage growth over recent years, which has been larger than implied by the historical
relationship with the unemployment rate. Of course, we may be justified from time to time to
think that history is likely to be a poor guide, but it's worth being explicit about any such
deviations. Finally, while models play an important role in the forecasting process, the value of
models should not be overstated, particularly because no one model that we have captures all
of the relevant features of an economy or consistently "beats" other forecasts.4
Central forecasts
Central forecasts attract a great deal of attention and commentary from financial market
participants, the press and the general public. The extent of that attention is often unwarranted.
As several of my colleagues have noted over the years, point forecasts should be treated with
a healthy degree of scepticism.5 It is unlikely that GDP growth or inflation will exactly match
point forecasts, or even narrow ranges around those points. Instead, central forecasts are best
thought of as our view of the most likely of a wide range of possible outcomes, with small
changes in the forecasts unlikely to reflect anything more than a modest shift in the balance of
risks.
To avoid fallacies that can accompany false precision, we recently altered what we refer to as
"Table 6.1" of the forecasts in the Statement by presenting ranges for GDP and inflation in
1⁄2 percentage point increments, rather than 1⁄4 percentage point increments (shown below). No
doubt that will not discourage some readers from using their rulers to measure the graphs and
focus on revisions down to the nearest 0.1 percentage point! Also, some commentators will be
tempted to draw attention to what they might describe as "large 1⁄2 percentage point changes"
when the forecasts are revised, even though any such revisions may reflect much smaller
adjustments if it is the case that the forecasts have merely crossed rounding barriers.6 The key
point I'd like to make here is that if we judge any forecast revision to be of substance worth
noting, we'll note it!
To emphasise this point further, it is worth remembering that the available data are subject to
a degree of measurement error. In the case of real GDP, for example, quarterly growth rates
can easily be revised up or down by 1⁄2 percentage point or more in the first four years after the
initial estimate (Graph 1).7 The recent revision to GDP growth - of 0.2 percentage points in the
September quarter of 2015 - was relatively minor, though the data now suggest that GDP
growth picked up in the second half of 2015 to be more in line with the strength that was
apparent in a range of indicators of the labour market and business conditions at that time.
Starting points
A critical element of forecasting is to have a sense of where the economy is now and in which
direction it's currently heading. This comes from carefully dissecting the incoming economic
data in an attempt to disentangle signals from noise and determine the extent to which shocks
will be long-lived or transitory.8 In addition to publicly available data, we make use of
information obtained from our business liaison program. We also use information gleaned from
econometric models, which include both single-equation models of individual variables as well
as larger models that attempt to capture the behaviour of multiple variables simultaneously.
The various information sources that we use don't always provide a clear message about
where we are and where we are heading. Indeed, it is naïve to think that the truth can reside
in a single source of data or a particular model. To an extent, this reflects the usual noise in
the various types of information, as well as uncertainty about the strength of different economic
relationships. Combining information from a variety of sources, and models, typically results in
more robust conclusions than relying exclusively on a single source.
The behaviour of inflation provides a timely example. A wide range of information suggests
that inflation is low and likely to remain so over the next couple of years. This includes both
structural (DSGE) and statistical (VAR) models of inflation. These attribute the outcomes over
the past year or more, in part, to the influence of foreign "factors" - as captured by low inflation
and low interest rates in the advanced economies. These can have a direct effect on inflation
in Australia via the prices of imports. There are indirect effects as well, whereby spare capacity
in product and labour markets globally may have contributed to relatively low inflation and
wage outcomes in Australia. The models suggest that these influences on Australian inflation
are typically quite persistent, which reinforces the message from other sources that inflation is
likely to remain low for some time.
Uncertainties
For several years we have presented confidence intervals for GDP growth and inflation
forecasts in the Statement (Graph 2).9 These summarise the extent of uncertainty based on
previous forecast errors. Since February of last year, we have also published an
unemployment rate forecast and confidence intervals around that.
In addition to confidence intervals, the forecasting process leads us to think about the risks
associated with specific economic developments and to quantify those where possible. Each
quarter, we discuss a range of scenarios that explore how the economy might respond under
conditions that vary from the central case. For example, what if commodity prices, the
exchange rate or overseas economic conditions evolve differently to the paths embedded in
our central forecasts? These exercises help us to identify events that could have a meaningful
effect on the economy and to which policymakers may need to respond.
Background to the Pagan-Wilcox review
We commissioned Adrian Pagan and David Wilcox to conduct a review of forecasting in
Economic Group in late 2014. We asked for their views on whether there were areas we could
improve upon, whether the tools of forecasting were the right ones and whether we were using
the forecasts appropriately. We did not commission the Review as a result of any concerns
about our recent forecasting experience or because we felt that our existing procedures were
fundamentally flawed. But it was time for a careful health check of our approach.
Professor Pagan and Dr Wilcox visited the Bank for two weeks during one of our regular
forecasting rounds. They spent time with individual sections in Economic Group, examining
our models and forecasting approaches in detail. They also met with senior management and
attended our internal forecast discussions.
Their review represents a thorough analysis of our forecasting procedures. We have made it
publicly available today. I will attempt to summarise its key conclusions and describe how we
have responded to the recommendations.
The review's conclusions and recommendations
The Review concluded that our forecasting practices were fundamentally sound and produced
information conducive to good policymaking. The reviewers praised the knowledge, motivation
and technical proficiency of our analysts. They also commended the use of models and the
spirit of open debate in our internal forecasting discussions.
But there is always room to improve. The Review made a number of recommendations, which
can be summarised in three categories: the development of new models; changes to forecast
procedures; and organisational changes.
Development of new models
There were two recommendations regarding the modelling tools we used.
• First, the Review recommended that we put additional resources into developing and
analysing "full-system" or "general equilibrium" models of the economy - that is,
models which account for the simultaneous responses of a large number of key
variables to unexpected developments (or shocks).
The Review recommended supplementing our existing models with one that
incorporates our separate single equation estimates for a range of key variables into
a system of equations.10
• Second, the Review noted the enormous changes to the structure of the Australian
economy over the past decade or more, particularly, but not exclusively, related to the
mining boom. These developments caused shifts in the composition of economic
activity, such as the large pick-up in mining investment that peaked in 2012. Hence,
the relationships between economic variables may be different from the past. The
Review recommended placing more emphasis on investigating the robustness of
existing economic models to structural change and provided a number of suggestions
on how to do this.11
Changes to existing forecast procedures
The Review also recommended some modifications to some of our forecast procedures.
Forecast horizon
It suggested we consider extending the forecasting horizon beyond two-and-a-half years. It
was acknowledged that doing so would be more straightforward when using model-based
forecasts. Moreover, the current approach has a number of advantages. A two-to-three year
horizon is a period over which monetary policy, and what could broadly be termed
"demandside" factors, tend to influence economic activity. Over longer horizons, "supply-side"
influences, like changes in the trend rate of productivity growth or the non-accelerating inflation
rate of unemployment (NAIRU), are likely to be more important. These factors are more difficult
to forecast than short-run, demand-side influences, and are not affected by monetary policy
decisions.
However, following large and persistent disturbances, such as a once-in-a-century boom in
commodity prices, the economy may not return to its steady state within the existing forecasting
horizon. This could complicate the assessment of whether current policy settings are
appropriate, hence the recommendation to extend the horizon.
Cash rate paths
Another recommendation was related to a "technical assumption" about the cash rate that
underpins our forecasts. At the time of the Review, we typically assumed that the cash rate
10
I should note that full-system models of one sort or another have a long history at the Bank and already play a
role in the forecasting process. The earliest examples of such models date back as far as the 1970s and include
the RBA1 model (Henderson J and P Norman (1975), "The Equations of the RBA1/74 Model of the Australian
Economy", RBA Research Discussion Paper No 7504) and the RBA76 model (Jonson P, E Moses and C Wymer
(1976), "A Minimal Model of the Australian Economy", RBA Research Discussion Paper No 7601). In the late
1990s, Economic Research Department staff constructed a small empirical model of the Australian economy.
This model was documented in Beechey M, N Bharucha, A Cagliarini, D Gruen and C Thompson (2000), "A
Small Model of the Australian Macroeconomy", RBA Research Discussion Paper No 2000-05 and Stone A, T
Wheatley and L Wilkinson (2005), "A Small Model of the Australian Macroeconomy: An Update", RBA Research
Discussion Paper No 2005-11. More recently, our staff have developed more complex dynamic stochastic
general equilibrium (DSGE) models. These models were documented in Jääskela J and K Nimark (2011), "A
Medium-scale New Keynesian Open Economy Model of Australia", Economic Record (87), pp. 11-36 and Rees
D, P Smith and J Hall (2015), "A Multi-sector Model of the Australian Economy", RBA Research Discussion
Paper No 2015-07. These sorts of models feature in our internal forecast meetings. However, I think it is fair to
say that full-system models have not been fully integrated into our forecasting procedures and the nature of
those models has been somewhat distinct from the single-equation models that most of our analysts work with
day-to-day.
11
For example, by estimating models incorporating time-varying coefficients or using techniques such as
exponential smoothing that reduce the weight given to certain observations when estimating the parameters of
a model.
would remain constant across the forecast horizon.12 This has the advantage of simplicity.
Also, over short horizons it often provides a reasonable approximation to what econometric
models and financial market participants would expect. However, over longer horizons a
constant cash rate assumption may be less plausible, particularly when interest rates are far
from average levels. The Review recommended considering alternatives to a constant cash
rate assumption. A number of alternatives exist, including: the path implied by financial market
prices; a path consistent with the past behaviour of the Bank as summarised by an estimated
"monetary policy rule"; or an ad hoc path postulated by staff members. There is no consensus
about which of these alternatives is optimal.13
Broaden discussion of the forecasts
The Review also made suggestions relating to the presentation of our forecasts in the
Statement. In particular, the authors recommended that we publish a forecast for the
unemployment rate. They noted that to assess the state of the real economy it is not sufficient
to know the pace of GDP growth; one also needs to know how this rate of growth relates to
potential growth and hence the extent to which spare productive capacity is rising or falling.
While we have always focused on these concepts in our internal analysis, publishing
unemployment rate forecasts provides useful information in this regard.
Discussion of the risks
An additional suggestion was to alter our discussion of risks to the forecasts in the
Statement.
The Review argued that there was scope to provide more guidance on the plausibility and
implications of alternative scenarios, rather than merely providing a list of events that could
affect the outlook.
It is possible to come up with any number of scenarios that may cause economic outcomes to
differ from a given set of forecasts. It is worth noting, however, that many plausible scenarios
may have fairly benign implications. To give one example, we typically condition our forecasts
on a constant exchange rate, even though it would be unusual for the exchange rate to remain
steady for any length of time. However, the effect of an exchange rate movement will depend
in large part on whether it has occurred in response to other developments, such as a change
in commodity prices. The consequences of such exchange rate movements are predictable to
some degree and, in many instances, have tended to help insulate the economy from adverse
developments offshore or even domestically. In other circumstances, a large exchange rate
movement (or even a lack of movement in the face of other developments) may represent an
important shock to the economy.
One can also imagine scenarios that are unlikely to occur but may have far more substantial
implications for the economic outlook if realised. These scenarios can be difficult to quantify
but may be worth discussing nonetheless. An example that we discussed in our most recent
Statement was the potential for financial instability in China to lead to a sharp slowdown in
economic activity there and in the Asian region more broadly.
Organisational suggestions
Finally, the Review made some recommendations regarding the organisation of Economic
Group.
The first was to establish a section dedicated to the development and use of full-system
macroeconomic models. It would build upon the work of the existing modelling team and
develop a new model. The establishment of such a section would facilitate the greater use of
models within the Bank, increase cooperation between different sections generating the
forecasts and help to enhance the familiarity of our staff with these types of models.
The Review also encouraged the Bank to consider whether existing hiring and staffing policies
encouraged the right mix of generalists and technical specialists. In particular, it highlighted
macroeconomic modelling as an area requiring technical expertise. In addition, the Review
questioned whether personnel across Economic Group were distributed optimally, with the
suggestion that more staff could be dedicated to modelling if there were fewer staff monitoring
overseas economies and/or participating in the Bank's Regional and Industry Analysis section,
which conducts liaison across the country.
Responses to the review
My colleagues and I have spent time discussing the Review's conclusions, re-examining our
existing procedures and developing appropriate responses.
We have already implemented some of the Review's recommendations:
We have added a quantitative discussion of our unemployment rate forecasts to the
•
Statement, with a graph of confidence intervals to illustrate the extent of uncertainty.
• The Statement's Outlook chapter now provides a more comprehensive explanation of
the uncertainties around our forecasts, including more information about the channels
through which risks could affect the economy.
• We have changed the nature of the cash rate assumption underpinning our forecasts.
Since the start of 2015, we have conditioned our forecasts on the assumption that the
cash rate moves broadly in line with the path implied by financial market pricing.
• To increase the Bank's capacity to use full-system models, we have established a
new Macroeconomic Modelling section within the Economic Analysis Department.
This has primary responsibility for generating model-based analysis to enhance the
quality of our forecasting processes and policy advice. I should emphasise that this
section will complement our existing forecasting processes, not replace them. As is
the case at many other central banks, our forecasts will still be generated by a range
of analysts and will feature a degree of judgement, rather than be mechanical,
modelbased forecasts. However, the forecasts will be usefully informed by the insights and
analysis that full-system models can provide.
The Review recommended redirecting resources from monitoring overseas economies and
conducting business liaison to other activities, particularly modelling. However, we have
sourced staff for the new modelling section from across Economic Group more broadly and
have no intention of reducing the extent of our liaison program. This reflected our assessment
that the benefits of monitoring overseas economies and conducting domestic economic liaison
go well beyond the direct contribution to our quarterly forecasting process.14 In addition, the
liaison program provides valuable insights for forecasting. For example, during the mining
investment boom, liaison provided timely and accurate information about construction projects
that was not available elsewhere. Combining our liaison on each project has provided a
reasonably accurate picture of what has transpired. Moreover, because a commodity price
boom of this magnitude had not been experienced before, models estimated using historical
data would have had difficulty anticipating the extent of the response of mining investment.
Similarly, our analysts monitoring overseas economies help us to understand the economic
and financial developments affecting our trading partners, particularly in the Asian region, in a
way that would not be possible from using publicly available forecasts of a limited range of
variables such as GDP and inflation from organisations like the International Monetary Fund
or Consensus Economics.
Conclusion
The Pagan-Wilcox Review was a comprehensive health check of our forecasting approach.
While the Review confirmed that our methods are fundamentally sound, it provided a number
of valuable suggestions for how we could improve the way we forecast. We have already
responded to many of the suggestions and are in the process of following up on others. While
these changes are unlikely to see much of an improvement in forecast accuracy, they have
the potential to enhance the role that forecasting plays in the policy process and facilitate the
usefulness of the forecasts as an important tool of communication.
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