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Timing, high frequency of release and non-revision make PMI
data an ideal tool for policymakers
Relationship between PMI and economic activity provides basis
for modelling central bank behaviour
Logit models based on PMI output and price data offer insight
into policy-making process
In this short research paper, we showcase how the Purchasing
Managers' Index® (PMI®) datasets
can be used to help understand and predict changes in European
monetary policy decisions.
Using a similar approach to Gerlach (2017), we create a
PMI-based 'logit' model using a simple "Taylor rule" approach to
assign probabilities to the range of possible European Central Bank
(ECB) and Bank of England (BoE) policy-decisions since 1999. [*] We find that the model performs strongly
in anticipating rate decisions and highlights the positive role
that PMI data play in rate-setting circles. Our research suggests
that PMI readings below 52.0 are generally commensurate with lower
policy, while reading above 56.0 are consistent with tighter
policy. We also find the ECB is more likely to respond to price
pressures than the BoE, whose primary policy focus rests on output
growth.
Why use the PMI to support policy?
The ability to understand current economic performance in 'near'
real-time is an essential component of the monetary policy
decision-making process.
By utilising high frequency, non-revised and quickly-released
data on economic performance, confidence in the likely direction of
the economy can be built. Policymakers can subsequently optimise
changes in key macroeconomic management levers such as interest
rates.
The PMI data series, now produced by IHS Markit in over 40
countries, meets all the aforementioned characteristics of a
desirable economic indicator.
Derived from a questionnaire sent to fixed panels of carefully
selected business executives across both manufacturing and service
sector industries, the PMI datasets provide
monthly information on a wide variety of metrics
such as output, new orders, employment, prices and stocks.
Typically conducted in the middle of the month, results from the
surveys are also released quickly following the
end of the monthly reference period, with data provided on either
the first working day (manufacturing) or third working day
(services and composite aggregations of both sectors). [*]
Finally, PMI data are rarely revised. Unlike slowly produced
official figures such as GDP, numbers for which are subject to
notable future reconsideration, PMI historical values are never
restated or revised. This feature of the PMI dataset adds an
additional layer of confidence for policymakers when making
decisions.
Figure 1 demonstrates how the PMI has captured variations in the
euro area business cycle since data were first available in 1999.
The bursting of the 'dot-com' bubble saw the PMI drop sharply at
the turn of the millennium. The economic upturn that followed ended
when Lehman Brothers collapsed, and the sub-prime mortgage crisis
ensued. The impact of the eurozone sovereign debt crisis that
subsequently escalated is also illustrated by a steep output
decline in 2011.
Prices data for the single-currency bloc also highlight the
inflationary trends which have been apparent since 1999,
particularly between 2008 and 2011, when higher price pressures
were driven primarily by the cost of oil.
The various economic gyrations over the past two decades are
equally apparent in PMI data for the UK, as highlighted by figure
2. Most recently, in the immediate aftermath of the EU referendum
in July 2016, the Composite Output PMI dropped to the lowest since
2009, which followed with the Bank of England cutting the base rate
in its August meeting. The depreciation of sterling following the
Brexit result is also captured by the Input Prices PMI rising
steeply throughout late 2016.
PMI Policy Model
We now consider more deeply the question of whether the PMI
dataset contains useful information about ECB and BoE rate-setting
behaviour by including the data in a simple econometric model.
Our model is based on a multinomial logit specification, which
allows probabilities to be assigned to the three central bank
policy outcomes of either loosening, tightening or leaving monetary
policy unchanged. Note that we treat decisions around "quantitative
easing", which came into force following the financial crisis in
2008, as equivalent to an interest rate change.
Based on the assumption that central banks respond to changes in
either economic activity or pipeline price developments, we include
both the composite PMI output and input price indices as
explanatory variables in our models.
The two figures below show results for both the ECB and BoE
since 1999.
For both central banks, the model's implied probabilities of
rate decisions have generally coincided with actual rate decisions.
Most notably, the rate tightening cycle in the euro area around the
turn of the millennium, followed quickly by the loosening in policy
during the bursting of the 'dot-com' bubble, is tracked well by the
models.
Similarly, during the run-up to the financial crisis, higher
probabilities for rate tightening were signalled, before the rapid
shift to markedly looser policy after the collapse of Lehman
Brothers in September 2008. In the euro area, rate cuts were again
the order of the day as the sovereign debt crisis intensified from
2012 onwards.
Indeed, as the PMI Input Prices Index posted levels in the high
60s during the first half of 2011, the model assigned a high
probability to the two rate increases that the ECB enacted during
this period. The BoE, in contrast, was shown to be much more likely
to see through perceived temporary spikes in inflation and hold
rates at the then-record low of 0.5%. The BoE appears more driven
to loosen policy in response to weak activity data.
PMI Policy Model Results
From the model outputs, it is possible to compute a set of
matrices detailing implied probabilities of policy changes given
values for the composite business activity and input price
variables.
If we consider a situation where the UK economy is growing
sharply, the implied probability of tightening policy shifts
remarkably higher. Holding input prices constant at 58.0 (the
long-run average for the UK), a rise in the Output PMI from 60 to
62 induces a 24 percentage point rise in the implied probability of
tighter policy to 57% from 33%. Indeed, the largest shifts in the
implied probability come when activity growth accelerates. As
stronger inflation is generally a lagged response to faster
economic expansion, it comes as no surprise that the BoE is likely
to be more pro-active when economic momentum picks up.
Similar policymaking behaviour can be seen when the UK economy
has entered a downturn. Again, taking the Input Prices PMI at its
long-run average (58.0), a faster contraction in activity yields
the largest increases in the implied probability. A drop in the
Output PMI to 44 from 48 results in the implied probability of
additional stimulus rising by around 30 percentage points to
67%.
Overall, the model indicates that economic growth momentum is
the key driver behind BoE rate decisions. As can be seen in the
above tables, weak activity growth, irrespective of price
pressures, is typically associated with high probabilities of
stimulus, while the likelihood of tighter policy is greater when
economic activity is rising sharply, even when inflationary
pressures are weak.
Turning attention to the model results for the ECB, differences
from the BoE in monetary policy-setting behaviour are apparent. The
model captures the inflation-averse nature of the ECB, which
becomes palpable when looking at the extreme variation in the
implied probabilities of a rate increase for different values of
the Input Prices PMI, holding activity constant.
According to the model, the ECB is four times more likely to
tighten policy than the BoE when the Output and Input Prices PMI
are at 54 and 66 respectively. Furthermore, when economic activity
is expanding sharply (Output PMI at 64) but inflationary pressures
are fading (Input Prices PMI at 48), the implied probability of a
hike by the ECB is just under 2%, but over 50% in the BoE's case.
This further implies the ECB's preference for controlling inflation
irrespective of contemporaneous output developments.
For rate-cutting behaviour, some similarities between the ECB
and BoE are apparent. Values below 52 for the Output PMI generate
the strongest implied probabilities of easing policy. Nonetheless,
the inflation-averse bias of the ECB is still clear. Consider a
situation where the Output Index is at 48, but the Input Prices
Index is at 62. Under such circumstances, the BoE would be more
than twice as likely to ease monetary policy than the ECB.
Summary
Our analysis suggests that the probabilities of both ECB and BoE
monetary stimulus are generally higher when readings for both the
output and input price indices are in the low 50s, with
probabilities of looser policy rising sharply as we fall below 50.0
and beyond.
Policy tightening cycles seem more likely to occur when PMI
figures hit levels in the mid-to-high 50s. The switchover to rate
tightening territory is estimated to be around readings of 56.0.
Our results show the BoE is more responsive to changes in economic
activity, while the ECB demonstrates a greater reaction to
prices.
A "neutral" zone for policy is estimated to exist somewhere
between PMI readings of 52.0 and 56.0. Here the probabilities of
unchanged policy have tended to be greatest.
PMI data are timely, rarely revised and are closely correlated
with underlying economic activity. These features of the PMI
datasets subsequently make them an ideal tool for those wishing to
understand recent economic developments. This is especially the
case in monetary policy circles, given PMI data provide the
earliest indication of macroeconomic performance globally.
Based on this observation we subsequently adapted a 'barebones'
Taylor rule approach to a simple multinomial logit model to assign
probabilities to likely ECB and BoE policy decisions based on the
historical relationship between PMI data and actual decisions. Our
simple model provides a basic framework for using PMI data in
thinking about likely central bank policy movements.
The model could be adapted to include features that cover the
extraordinary period following the financial crisis (when central
banks were more likely to loosen policy or leave rates unmoved as
they hit the zero bound). Economic momentum and the relative
position of the economy in the business cycle could also be
incorporated. We leave such adaptations for future research.
The results of our analysis are encouraging and provide
persuasive evidence that central bankers pay close attention to the
monthly PMI results when formulating their policy decisions.
Purchasing Managers' Index™ (PMI™) data are compiled by IHS Markit for more than 40 economies worldwide. The monthly data are derived from surveys of senior executives at private sector companies, and are available only via subscription. The PMI dataset features a headline number, which indicates the overall health of an economy, and sub-indices, which provide insights into other key economic drivers such as GDP, inflation, exports, capacity utilization, employment and inventories. The PMI data are used by financial and corporate professionals to better understand where economies and markets are headed, and to uncover opportunities.
