Speech
Beware False Prophets
Andrew Hauser
[
*
]
Deputy Governor
Speech to the Economic Society of Australia (Queensland)
Brisbane
–
12 August 2024
Audio
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Q&A Transcript
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Watch video: 'Beware False Prophets', Speech by Andrew Hauser, Deputy Governor, to the Economic Society of Australia (Queensland)
Introduction
My theme today is learning and the role it plays in monetary policymaking. 1
So it is fitting that I am speaking with you, on my first visit to Brisbane, just a stone’s throw
from two great seats of learning – the Queensland University of Technology (QUT) and the University
of Queensland (UQ).
QUT’s Gardens Point campus sits on a stunning peninsula in the Brisbane River that is full of
history. The Botanic Gardens started life in 1828 as a means of feeding the penal colony. And Old
Government House was built in the 1860s for the first Governor of Queensland, Irishman Sir George Bowen
and his wife, the tremendously-titled Contessa Diamantina di Roma (after whom the Diamantina River is
named). 2
But the history of this area, and its connection with learning, is much older than that. The river –
or Maiwar as it was long known – has been a source of life for the Jagera and Turrbal Peoples for
thousands of years. And the area around Gardens Point was a sacred site, where Aboriginal women received
ceremonial teaching – and men were forbidden. Today QUT is establishing a new Faculty of Indigenous
Knowledges and Culture to recognise and foster Indigenous Australian excellence and innovation. 3
Against
that rich backdrop, I want to acknowledge the traditional owners of this land, pay my respects to their
Elders past and present – and extend that respect to any Aboriginal or Torres Strait Islander
peoples here today.
Head south-west down the river to St Lucia and UQ’s Great Court and look upwards, and you may see a
carving of a jovial man in a hat carrying an apple. The gargoyle is said to be Dr Colin Clark – a
prominent British disciple of John Maynard Keynes. Clark was recruited in the late 1930s by Queensland
Premier Bill Forgan Smith to help make the case in Canberra for more expansionist state spending, and
establish one of the earliest systems of national accounts. When Keynes heard that Clark intended to
settle in Australia, he tried to dissuade him, arguing that he would be more influential back in England.
But Clark was having none of it: Australia, he wrote, was ‘too remarkable an opportunity to be
missed’; its people had ‘minds which are not closed to new truths’, and the country would
‘show the world, in economics, politics, education and technology in the next twenty years’. In
what seems to have been the clincher, he noted that ‘economics ranks next after cricket as a topic
of public interest’. 4
Nearly 100 years later, I’d change little in describing my own early impressions of Australia,
with one notable exception: sport quite often plays second fiddle to economics (and the RBA) on the front
pages!
A vibrant public economic debate, with diverse views, is healthy. But one feature of the landscape is less
desirable – and that is the extraordinary
certainty
with which individual views about
the outlook for the economy and the path of monetary policy can sometimes be expressed. ‘The economy
is falling off a cliff’; ‘No – the economy is red-hot.’ ‘Rates up now –
we’re way behind the curve’; ‘Rates are clearly far too high – slash them’;
‘It’s vital rates stay where they are’. Those seeing things differently are castigated as
incompetent, biased or on the make. And changes in view are presented as disastrous or humiliating
failures. In short, it’s a world of winners and losers, gurus and charlatans, geniuses and buffoons.
This isn’t unique to Australia of course – everywhere is prone to it in some way (witness the
extraordinary calls for emergency intra-meeting rate cuts in the United States during the recent market
turbulence, for example).
Of course, eye-catching language sells newspapers, secures clients and draws crowds to the soapbox. But
when the stakes are so high, claiming supreme confidence or certainty over what is an intrinsically
uncertain and ambiguous outlook is a dangerous game. At best, it needlessly weaponises an important but
difficult process of discovery. At worst, it risks driving poor analysis and decision-making that could
harm the welfare of all Australians. It is right to want to be confident that the central bank will bring
inflation back to target and maintain full employment: that is the RBA’s mandate, and we should be
held to account for it. But the policy strategy required to deliver that outcome, and the economic
judgements that inform it, simply cannot be stated with anything like the same degree of certainty. Those
pretending otherwise are false prophets.
Overconfidence is not unique to economic commentary: it’s a universal human failing. 5
In my
remarks today, I want first to illustrate some real-world examples, before turning to ways in which
central banks can avoid falling prey to it in our own deliberations. By forming contingent hypotheses
about the future – rather than overly precise point forecasts. By learning continuously –
from our own forecast errors, from diverse quantitative models, from corporate liaison and other
qualitative intelligence-gathering, from experience in other countries, and from internal and external
challenge, including scenarios and ‘what-ifs’. By communicating clearly and openly about what
we don’t know, as well as what we do. And by adopting policy strategies that reflect risks to the
outlook, as well as the central case. I will describe how some of these tools were applied in our most
recent monetary policy round, and how we hope to develop them further.
Overconfidence in everyday life
To illustrate how commonly overconfidence arises in everyday life, let me pose four questions, most with a
Queensland flavour. I’m keen to know both your answers, and also your
confidence
in
those answers:
How many paving stones cover QUT’s Gardens Point campus?
Will it rain in Brisbane tomorrow?
Who will win the 2025 State of Origin rugby league series?
What will Elon Musk do next?
Question 1 provides scope for the greatest certainty, because there is a precise, knowable answer. At
the same time, it’s not an
easy
question – few of us have intimate knowledge of
the campus; fewer still are quantity surveyors or have the time to go and count them! So while we can
make more or less informed guesses, we are likely to cite wide ranges of uncertainty around those
guesses.
What’s striking, however, is that, while we may think the ranges we give are wide, they are actually
almost always too
narrow
. Table 1 reports the results of an exercise in which
professionals across a range of industries were asked a set of hard but factual questions like the one
I’ve just posed. The column on the right shows how often people’s ranges of uncertainty are too
narrow to include the true answer. So, for example, only 10 per cent of those asked to give
90 per cent confidence intervals should miss the true answer. In fact, miss rates lie between
42 and 64 per cent. People are vastly too confident about what they truly know.
Table 1: Overconfidence across Industries
*
Percentage of misses
Industry tested
Kinds of questions used in test
Ideal (%)**
Actual (%)
Advertising
Computers
Data processing
Money management
Petroleum
Pharmaceutical
Security analysis
Source: Russo JE and Schoemaker PJH (2016), ‘Overconfidence’, in M Augier and D Teece (eds),
The Palgrave Encyclopedia of Strategic Management
, Palgrave Macmillan, London,
p 1238.
* There is also a good discussion of this issue in Part III of Kahneman D (2011),
Thinking, Fast and Slow
, Farrar, Straus and Giroux, New York.
** The ideal percentage of misses is 100 per cent minus the size of the confidence interval.
Thus, a 10 per cent ideal means that managers were asked for 90 per cent confidence intervals.
In case you are curious, by the way, the Gardens Point campus is covered by 9,546 Urbanstone Juperana
granite pavers. I know this, not because I am a paving nerd, but because the company that provided the
materials is part of the RBA’s liaison program, through which we keep a close eye on real-world
economic developments – and a key plank of our continuous learning process that I will elaborate on
later.
Question 2 asked ‘Will it rain in Brisbane tomorrow?’. Unlike the previous question,
there’s no certain answer. But we
can
put quite well-defined probabilities on it.
History suggests that it rains on just under 20 per cent of August days (Graph 1).
Graph 1
But we can do better than that: the Bureau of Meteorology (BOM) uses much richer data and modelling to put
the probability of rain tomorrow at a rather alarming 95 per cent (Figure 1)!
Figure 1: BOM Forecast for Weather in Brisbane Tomorrow
Source: BOM, ‘Brisbane Forecast’, Brisbane Forecast (bom.gov.au), as of Sunday
11 August 2024.
Unlike most of us, weather forecasters are actually quite good at estimating their uncertainty about their
short-term forecasts. Even as far back as the 1970s, if they told us there was a 95 per cent
chance of rain tomorrow, they were usually pretty close (Graph 2). And meteorological models have
evolved enormously in sophistication and accuracy since then. 6
Graph 2
At first sight, Question 3 – ‘Who will win the State of Origin rugby league series in
2025?’ – seems similar to Question 2. There are only two possible answers 7
–
and probabilities seem readily available, for instance from betting markets (Graph 3). But the
similarities are illusory. Today’s short-term weather forecasts are the product of advanced
stochastic modelling that yields well-defined risk measures. But no model can fully capture the huge
range of time-varying, ambiguous and judgemental factors that go into determining the winner of a sports
series a year out. Such processes are said to be subject to ‘Knightian’ or ‘radical’
uncertainty. 8
Graph 3
Even in the short gaps between this year’s games, the quoted odds bounced around like a yo-yo in
response to a stream of unpredictable developments – Joseph Sua’ali’i being sent off in
game 1; Latrell Mitchell’s triumphant return for NSW in game 2; and the pitched battle of game 3.
Who knows on what, as-yet unimagined, events next year’s series will turn. But it seems likely that
the current
60/40
odds quoted in favour of NSW reflect excessive
confidence that whoever won in 2024 will win in 2025 too, rather than any clear-eyed assessment – a
variant of the certainty bias.
My final question – ‘What will Elon Musk do next?’ – is a more extreme form of
radical uncertainty. Key drivers of the true answer – goals, beliefs and motivations – are
partly or wholly unobservable (so-called ‘latent’ variables). And the range of possible answers
is unbounded. Anyone claiming to have identified a small number of well-defined outcomes, each with a
clean probability, is at best ambitious – and, at worst, at risk of seriously overestimating their
true confidence levels.
Tackling uncertainty and overconfidence in central banking
So what, then, for monetary policymaking?
When we set interest rates, we have to look ahead – that is, make forecasts. 9
That’s one source of uncertainty. Monetary policy works with long and variable lags – so our
forecasts have to be medium-term, not short-term. That’s a second source of uncertainty. And most
importantly of all, the things we are forecasting – inflation and unemployment – are the
complex, time-varying outcomes of the decisions and interactions between many millions of people,
companies and other organisations.
That puts us squarely in the world of Knightian uncertainty – of the State of Origin or Elon Musk.
Unfortunately, most of the models used for economic analysis come from the worlds of paving stones and
tomorrow’s weather! Estimate the average historical relationship between individual economic
variables, run those relationships forward in time – and you get a set of deterministic
point-estimate forecasts. Such model-based forecasts help show how the economy might respond if
relationships remained exactly as they were in the past. But over-reliance on them has two key drawbacks:
First, the probability of such paths being precisely correct is essentially zero. That makes them a
poor basis for decision-making when used in isolation – because the absence of alternatives or
fallbacks makes it harder to conceptualise other possible outcomes and weigh up the likelihood of
those alternatives becoming reality. And that, in turn, can force forecasters either to underreact to
news (forcing the data into their existing narrative, rather than learning) or to overreact
(jettisoning one point-forecast for another demonstrably different path, which will also prove to be
wrong).
Second, although it is possible to estimate confidence intervals around point forecasts using the
models themselves, they are likely to be substantially too narrow (that is, overconfident) because
they fail to account for the fact that the model may simply not be the right representation of
reality. 10
So just as overconfidence can easily affect our everyday lives, it can affect monetary policymakers too.
How do we guard against that?
The starting point is to avoid placing too much reliance on point forecasts in the first place, and
instead frame our policy decisions in terms of contingent hypotheses or judgements. Some judgements may
be strongly held, and hence given a high weight in the decision; others may be very tentative and given
only a low weight. Both the hypotheses, and the weights attached to them, are continuously updated
through a process of learning.
To bring this to life, consider a key question in the run-up to the RBA’s most recent policy
decision:
why has CPI inflation been so unexpectedly persistent?
A good place to start is to ask how unexpectedly persistent inflation has actually been – that is,
to
learn from our own forecast errors
. Such learning can be difficult for those who
treat forecast ‘misses’ as failures. A more mature approach – and one long adopted by the
RBA and other central banks – seeks instead to recognise that where forecasts are carefully
constructed to make the best use of current data, ‘misses’ contain vital information about an
intrinsically complex and stochastic world. This idea is also embedded in the ‘fancharts’ shown
around the RBA’s outlook for inflation and other variables, which are calibrated using past forecast
errors, not mechanical model outputs. 11
Three key facts emerge when we look at the recent path of forecast errors for inflation. First, although
underlying inflation for the June 2024 quarter was broadly in line with our May forecast, inflation in
earlier periods in
2023–2024
proved somewhat stickier than we had expected (Graph 4).
Graph 4
Second, the pattern for market expectations (derived from inflation swaps) has been somewhat similar,
suggesting the upside surprises have not been limited to the RBA (Graph 5). 12
Graph 5
Third, some other jurisdictions have seen a rather greater incidence of inflation
under
shoots
in recent quarters, raising the possibility that something slightly different might be underway in
Australia. Graph 6, for example, shows the picture for the euro zone. 13
Graph 6
To understand why past inflation outcomes may have been stronger than expected requires a hypothesis. In
such circumstances, the economist’s reflex is to reach for a model: often some variant of a Phillips
Curve, in which higher inflation reflects some combination of (a) higher
expected
inflation,
(b) higher demand, and (c) lower supply capacity. There is no evidence that longer term inflation
expectations rose over this period in Australia. And GDP came in at or below forecast – which we
tentatively assume is informative about demand conditions. We have therefore placed some weight on the
possibility that past upside inflation surprises may have reflected somewhat weaker
supply
than previously thought.
Now, it is one thing to hypothesise weaker supply, it is another to quantify it. And that’s because
supply is not directly observable: it is a classic latent variable. 14
So any estimate is subject to
huge uncertainty. In the most recent monetary policy round, RBA staff approached this challenge in two
ways.
The first was to
use a range of alternative models
that posit different assumptions
about the structure of the economy to estimate supply. 15
It is common practice to transform the
results of such models into estimates of ‘spare capacity’ in either the labour or product
markets. Graph 7 shows the range of spare capacity estimates for unemployment implied by the range
of models. 16
Choosing a single path within this range is subject
to the very objections to point estimates that I just rehearsed. But to rationalise recent above-forecast
inflation outcomes, the August 2024
Statement on Monetary Policy
(SMP) assumes that supply
was somewhat weaker, and hence the labour market was somewhat tighter, than previously thought. 17
And
that extra weakness is assumed to persist, pushing up a little on the outlook for inflation. It must be
said, however, that these changes in assumptions are tiny relative to the huge true range of uncertainty
over these measures. So we have to be humble about our confidence in this judgement: spare capacity could
easily be much higher, or much lower. 18
Graph 7
As a second cross-check, staff also compared their estimates to
qualitative real-world
indicators
of capacity pressures from company surveys and the RBA’s liaison
program. 19
Graph 8 shows that such measures lie at or
above the upper end of our model-based estimates. That is also consistent with the messages of
persistently elevated cost pressures I have personally heard from liaison visits to Townsville, Perth,
Adelaide, Melbourne, Western and central Sydney in recent months.
Graph 8
Future inflation also depends on the outlook for demand – and here too we must guard against the
risk of overconfidence. In the latest SMP, we do so in part by considering
alternative
scenarios
, drawing on
lessons from overseas
(particularly important for
open economies such as Australia) and the
potential for, potentially sharp, changes in
behaviour
.
One scenario asks whether the unemployment rate may be about to pick up more rapidly than assumed, pulling
down on demand and reducing inflationary pressure. 20
In the central projection, employment
growth only falls modestly, with total hours worked in the economy continuing to rise as the population
and participation in the labour market grow. That is consistent with past downturns in activity of
similar magnitude (Graph 9).
Graph 9
But those assumptions could be wrong. What if unemployment rises more rapidly, as it has for example in
Canada, Sweden and New Zealand (Graph 10)? Concerns that something similar might be about to happen
in the United States caused last week’s sharp repricing in US rate expectations.
Graph 10
Graph 11 illustrates that a higher unemployment path would bring inflation back to the midpoint of
the target range more rapidly, more than offsetting the higher inflation persistence assumed in the
central case.
Graph 11
We also conducted a scenario exercise for household consumption. 21
Judgements about consumption
are particularly important for the inflation forecast, because consumption accounts for roughly half of
total Australian demand. Assuming that unemployment rises only gradually, the central projection is for
consumption growth to pick up in line with an expected recovery in household real incomes
(Graph 12).
Graph 12
The drivers of the pick-up in incomes are relatively clear, barring further shocks. But consumption also
depends on the extent to which households choose to spend or save these higher incomes – and we are
much less confident in that judgement.
It is possible, for example, that households fortunate enough to have accumulated assets in recent years
may choose to spend rather more than past relationships suggest. Aggregate net wealth in the household
sector has increased by 58 per cent in the past five years (Graph 13) – much more
than incomes. That could pose upside risks to consumption.
Graph 13
At the same time, other households, particularly the young, have been forced to cut their spending back
sharply as real incomes have fallen (Graph 14). Higher future real incomes may relax some of those
constraints, allowing such households to increase their spending by more than historical relationships
would suggest. But it is also possible that persistent uncertainty about the future may cause them to
rebuild savings or pay down debt, posing downside risks to consumption.
Graph 14
These two-sided uncertainties about future saving behaviour are captured in two stylised profiles. One
assumes that the saving ratio remains close to its relatively low current level; the other assumes it
picks up to around its longer term historical average (Graph 15).
Graph 15
As with the other judgements, it is notable that quite small differences in this assumption – well
within historical ranges – produce materially different outcomes for inflation and unemployment
(Graph 16).
Graph 16
Policy implications and conclusions
Let me conclude.
As humans, we are all prone to overconfidence, particularly when forecasting the future. In many cases,
the answer we ought to give is that we simply do not know. But it can be hard to say that, particularly
if your job is to give a view, and particularly where even small changes in judgement can have
first-order effects on people’s lives.
Central bankers, and those who comment on us, are no exception to this universal human trait. In my
remarks today, I have set out some of the ways in which we can try to lean against overconfidence without
falling into the opposite trap of saying nothing at all. By avoiding over-reliance on point estimates and
instead framing our assessments in terms of contingent hypotheses. By continually adjusting the weights
we place on those hypotheses through a process of learning – from our own forecast errors, from a
wide range of analytical models, from qualitative data and intelligence, from other countries, and
through what-ifs and scenarios. And by communicating openly and honestly about where we are relatively
confident about the outlook (and where we are not), where we are seeking to learn, and the balance of
risks. Many of these tools are already in use at the Reserve Bank – and we will move further in
that direction as we complete the implementation of the RBA Review recommendations.
Of course the billion dollar question is how to map essentially uncertain judgements into policy
decisions. That is really the subject for another speech – but the short answer is one should seek
to choose strategies that are responsive to, and also robust to, your evaluation of the risks to the
outlook as much as to the central projection. In some cases, uncertainty may induce you to be less
activist – as you wait for more data, or try to avoid triggering tail risks through your own
actions. In others, it can induce you to be more activist: for example, the asymmetry of potential
outcomes underpinned the policy response in many countries during the
COVID-19
pandemic.
But beware anyone who claims it is obvious what to do – for they are false prophets!
Endnotes
I am very grateful to Alex Baker, Alex
Ballantyne, Michelle Bergmann, Michele Bullock, Natasha Cassidy, Selwyn Cornish, Ryan Couston,
Stephen Cupper, Tom Cusbert, Amelia Dakin, Iris Day, Anthony Dickman, Boston Dobie, Jacqui Dwyer,
Rachael Fitzpatrick, Bowen Hao, James Holloway, Jessica Hua, Callum Hudson, Sarah Hunter, Ben
Jackman, Brad Jones, Chris Kent, Liam La, Kevin Lane, Jeremy Lawson, Virginia MacDonald, Kieran
MacGibbon, Martin McCarthy, Alexandra Michielsen, Marcus Miller, Patrick Parrish, Mick Plumb, Max
Prakoso, Avinash Rajan, Ravi Ratnayake, Penny Smith, Hamish Sullivan, Joyce Tan, Megan Thomas,
Jake Thomson, Dmitry Titkov, Greg Tyler, Tom Williams, Michelle Wright, Joan Zhang, Zhan Zhang
and Min Zhou for their assistance with, or comments on, this speech.
[*]
Many of the themes in this speech have been
covered by RBA colleagues in the past. See, for instance, Stevens G (2011), ‘
On the Use of Forecasts
’, Address to the
Australian Business Economists Annual Dinner, Sydney, 24 November; Debelle G (2017),
‘
Uncertainty
’, 7th Warren Hogan
Memorial Lecture, Sydney, 26 October; Ellis L (2019), ‘
Watching the Invisibles
’, The 2019
Freebairn Lecture in Public Policy, University of Melbourne, 12 June; Ellis L (2022),
‘
The Neutral Rate: The Pole-star Casts Faint
Light
’, Keynote Address to Citi Australia & New Zealand Investment Conference,
Sydney, 12 October; Cagliarini A and A Heath (2000), ‘
Monetary Policy-making in the Presence of
Knightian Uncertainty
’, RBA Research Discussion Paper No
2000–10
. 1
Bowen had an extraordinarily varied career. After
his time in Queensland, he served as Governor of each of New Zealand, Victoria, Mauritius and
Hong Kong; and led a Royal Commission to draft the Maltese Constitution. 2
I am grateful to QUT Deputy Vice-Chancellor
(Indigenous Australians) Angela Leitch for this information. For further detail on the history of
the site and the goals of the new faculty, see respectively: QUT, ‘Campus to Country’,
available at <https://www.qut.edu.au/about/campus-to-country>; QUT, ‘Faculty of
Indigenous Knowledges and Culture’, available at
<https://www.qut.edu.au/about/faculty-of-indigenous-knowledges-and-culture>. 3
Millmow A (2021),
The Gypsy Economist: The
Life and Times of Colin Clark
, Palgrave Macmillan, Singapore, pp 73–83;
Markwell DJ (2000), ‘
Keynes and
Australia
’, RBA Research Discussion Paper No 2000-04. 4
A fascinating question for another speech is
whether economics may be particularly affected by overconfidence: Kozlowski AC and TS Van
Gunten (2023), ‘Are Economists Overconfident? Ideology and Uncertainty in Expert
Opinion’,
British Journal of Sociology
, 74(3), pp 476–500; Fox J
(2012), ‘Economists Are Overconfident. So Are You’,
Harvard Business
Review
(online), 27 June. And also, confidence amplified perhaps by its gender bias:
Sarsons H and G Xu (2015), ‘Confidence Men? Gender and Confidence: Evidence among Top
Economists’, 14 July. 5
A good read on these topics is Silver, N (2013),
The Signal and the Noise: The Art and Science of Prediction
, Penguin. It includes a
discussion of so-called ‘wet bias’, which describes the incentives that may bear on
some less honourable weather forecasters to lean towards
under
rather than
over
confidence, deliberately over-predicting the chances of rain, on the grounds
that people would rather go prepared for rain and be pleasantly surprised than the reverse! 6
I am reliably informed that this was not always
the case, because series draws were previously possible – and indeed occurred in 1999 and
2002 (in both cases Queensland retained the Shield because they won the previous series). The
rules were subsequently changed to introduce’golden point’, so now there has to be a
series winner. 7
See Knight FH (1933),
Risk, Uncertainty and
Profit
, 1st reprint edition, Houghton Mifflin Co, Boston; Kay J and M King (2020),
Radical Uncertainty: Decision-Making Beyond the Numbers
, WW Norton & Co, New
York. 8
In truth, we are usually uncertain even about
where we are today: macroeconomic data are typically released only with a lag, and are themselves
only estimates of the underlying concept, usually obtained by sampling or surveys and in some
cases prone to substantial revision. 9
There is empirical evidence that confidence
intervals formed by first choosing a central case and then overlaying two-sided intervals around
it are more vulnerable to overconfidence bias than confidence intervals chosen without such prior
‘anchoring’ to a central estimate: see, for instance, Kahneman D (2011),
Thinking, Fast and Slow
, Farrar, Straus and Giroux, New York. 10
For further examples of the RBA’s work in
this area, see: Tulip P and S Wallace (2012), ‘
Estimates of Uncertainty around the RBA’s
Forecasts
’, RBA Research Discussion Paper No 2012-07; RBA (2022), ‘
Box C:
What Explains Recent Inflation Forecast Errors?
’,
Statement on Monetary
Policy
, November; RBA (2023), ‘
Box B:
Has the Economic Outlook Evolved as Forecast a Year Ago?
’,
Statement on
Monetary Policy
, November. The importance of building such learning into monetary
policy deliberations in a systematic way was underscored in the recent Bernanke Review of
forecasting at the Bank of England: see Bank of England (2024), ’Forecasting for Monetary
Policy Making and Communication at the Bank of England: A Review’, 12 April;
Kanngiesser D and T Willems (2024), ‘Forecast Accuracy and Efficiency at the Bank of
England – And How Errors Can Be Leveraged To Do Better’, Bank of England Staff Working
Paper No 1078.
This obviously assumes that the market
projections are not simply copied from the RBA’s! 12
Experience in other countries is discussed by
Chris Giles in his recent
Financial Times
piece: Giles C (2024), ‘Learning to Trust the Forecasts’,
Financial Times
(online), 30 July. 13
Of course, demand is a latent variable too. And
there are many other ways in which the supply judgment could be wrong – most obviously if
the Phillips Curve is a poor model of reality. That is why it is important for policymakers to be
explicit about the judgments they are making, so the sensitivities of their projections to those
judgments can be understood and critiqued. 14
Most take signal directly from inflation or
wage out-turns, but many also incorporate other indicators that provide a read on supply, such as
labour productivity. For further information see Ballantyne A, A Sharma and T Taylor (2024),
‘
Assessing
Full Employment in Australia
’, RBA
Bulletin
, April. 15
The staff track a wide range of labour market
indicators to inform their judgement of capacity, see RBA (2024), ‘
2.5 Assessment
of Spare Capacity
’,
Statement on Monetary Policy
, August. 16
RBA (2024),
Statement on Monetary
Policy
, August. It is important to emphasise that the August assumption
retains the view that the labour market’s supply capacity has improved over the past decade
– just at a slightly slower rate than previously assumed. 17
The uncertainty surrounding model estimates of
the economy’s capacity are explored in Bishop J, J Hua, S Omidi, X Zhou and A Ballantyne
(2024), ‘
Assessing
Potential Output and the Output Gap in Australia
’, RBA
Bulletin
, July.
For further information on the liaison
program, see Bullock M (2024), ‘
Economic
Conditions in Post-Pandemic Australia with a Regional Lens
’, Rotary Club of Armidale
Annual Lecture, Armidale, 8 August. 19
RBA (2024), ‘
Chapter 3.4: Key
Risks to the Outlook
’,
Statement on Monetary Policy
, August. 20
See RBA (2024), n 20, ‘Key risk #2’.