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We introduce two new dynamic factor models to track economic
performance in the UK and euro area
Models provide unbiased, judgement free estimates of quarterly
changes in economic growth
Models rely heavily on PMI data as basis for creating business
cycle proxies
In this research paper we build on our previous nowcasting work with
Purchasing Managers' Index® (PMI®)
data by introducing two dynamic factor models that can be used to
provide judgement-free estimates of underlying changes in gross
domestic product (GDP) for the eurozone and the United Kingdom.
Our research provides two key takeaways.
Firstly, a factor derived from a dataset covering a vast array
of economic indicators, including business surveys, official
figures and financial conditions data, is loaded heavily onto our
own PMIs, reflective of the timeliness and close relationship that
exists between the PMI data and changes in quarterly GDP.
Secondly, the derived factor can subsequently be used to
calculate accurate and robust estimates of underlying GDP growth,
providing unbiased, judgement-free estimates of economic
performance in real-time.
Nowcasting: The Dynamic-Factor Model
Nowcasting, which is a process of measuring what's happening in
the economy today, or in the very near past or future, has garnered
an increasing amount of attention amongst policymakers, economists
and investors in recent years. However, faced with an increasingly
fast-paced economic environment, characterised by a large number of
data sources of varying quality, volatility and timeliness,
extracting a meaningful signal to track the economy in a timely
fashion remains a demanding exercise.
Dynamic-factor models (DFM) can help in this regard. By
extracting a single time-series "common-factor" from their
datasets, modellers are able to capture and summarise a substantial
proportion of the covariation between indicators. This factor can
then be used as a proxy of the business cycle and, in turn, make
judgement-free predictions about movements in gross domestic
product (GDP), the most widely-used yardstick of changes in
economic activity.
Crucially, the set-up of the statistical framework comfortably
deals with the dual demands of non-synchronous releases and
unavailability of lagging data sources e.g. industrial production,
retail sales data. The ability of the model to fill in the
dataset's so-called 'jagged edge' allows the maximisation of the
information content held within timely, reliable indicators and
allows the refinement of nowcasts in 'real-time' i.e. as and when
data sources are refreshed and updated.
Here in the Economics Indices team at IHS Markit, we have
followed the broad approach outlined above and constructed a model
that produces DFM-based GDP nowcast estimates for both the euro
area and the United Kingdom (UK).
Our nowcasting models utilise a variety of closely-watched
indicators that are commonly used by economists to track the
performance of these economies. The models cover various sectors of
the economy by drawing on business surveys, official output data,
labour market figures and information on financial conditions. A
full list of the indicators used in each model is provided in the
appendix.
Our historical GDP nowcasters for the euro area and the UK are
charted below against three-month-on-three-month changes in GDP.
Note we have used our models to produce monthly estimates of
official GDP growth between the official quarter end figures
(ensuring we have a monthly time series to compare against).
In both instances, the GDP nowcasters line up well against GDP.
For the eurozone, the correlation coefficient is 0.90 and for the
UK 0.76. Some of the missing explanatory power partly reflects the
smoother nature of our nowcasts: they tend to cut through some of
the volatility exhibited in the GDP series, especially in the UK
post-financial crisis era.
This is arguably a key feature of the dynamic-factor model:
these models can provide solid estimates of the underlying
performance of an economy, cutting through the noise of GDP figures
which can be impacted greatly by one-off, idiosyncratic events that
overly influence growth rates during a single quarter.
The strong build-up of inventories in the UK ahead of the (at
the time) planned March 29th UK departure from the
European Union (EU) is a topical example in this regard. Strong
stock accumulation in the first quarter of 2019 arguably provided a
"false" signal of the real position and health of the UK business
cycle: Inventory positions will likely be unwound in the months
ahead with the effect, all things being equal, of a lower UK growth
profile.
Notably, we find that the smoothness exhibited by the GDP
nowcasters reflect the strong contributions made to the models by
the respective Composite PMI data series. Given its timeliness,
plus its own strong relationship with GDP data, the models lean
heavily on the PMI and help to reduce the volatility seen in other
series, such as industrial production, trade, retail sales figures
etc.
Indeed, when tracked against each other, the nowcasters register
correlations of 0.91 and 0.92 for the euro zone and the UK PMIs
respectively. This indicates that PMIs are the best single source
summaries of regional business cycle conditions (see figures 3 and
4).
Model Performance
We now turn to the short-term predictive power of the DFM models
in anticipating quarterly changes in GDP.
Both of our models have been run in pseudo 'real-time' i.e. when
making predictions about GDP growth we mimic the broad data
structures that would have been available when the nowcasts are
made.[1].
For instance, when making a prediction in early June about GDP
growth for the second quarter of 2019, two months of business
survey data related to activity over the quarter would be
available. In contrast, there would be no information from official
data sources such as industrial production figures.[2]
As data availability increases through a quarterly cycle, the
nowcasts typically evolve. Our a priori expectations are
that nowcast accuracy will improve in line with the dataflow i.e.
the accuracy of the nowcast made just prior to the actual release
of GDP data will be more accurate than those made at the start of a
quarter when there is little or no data related to current economic
activity available.
We have taken three distinct nowcast snapshots: the first just
after the end of the first month of a quarter, the second around
the midpoint of the nowcasting cycle, and the third just before the
first release of GDP figures (when the data available is
highest).
Table 1 provides a summary of the model performances for the
euro area and the UK through a typical nowcasting cycle. Our
out-of-sample exercise covers the period 2014Q1 to 2019Q1. The
gauge of nowcast performance is the root mean square forecasting
error (RMSFE). Readings closer to zero should be viewed as the most
positive (i.e. containing the least error).
As expected, the RMSFE's generally improve through the
nowcasting cycle, illustrating how the availability of high
frequency data such as surveys and the flow of information are
important in reducing error. From the early nowcast to the final
nowcast, the gains in accuracy are 25% for the UK, and 32% for the
euro area respectively.
The RMSFE readings of 0.20-0.25 at the end of the nowcasting
cycle are also consistent with our reading of the academic
literature in the post financial crisis period - and indicate sound
model performance.
Where are we now?
We have been running our models in real-time on a fortnightly
basis since the turn of 2019. Here we discuss the latest nowcasts
and their evolution over the second quarter of 2019.
The recent message from our nowcasters is not especially
encouraging in terms of the health of the Eurozone and UK
economies.
The euro area economy, characterised by global trade worries and
political uncertainties, is set to register minimal growth at best
in the second quarter of the year.
Underlying growth is estimated to be running at a quarterly rate
of just 0.11%, a noticeable slowdown from the 0.4% increase seen
during the first quarter, and amongst the softest rates of growth
seen in the past six years - consistent with the recent messages
from the timely PMI data.
The region's key German manufacturing base remains a particular
source of weakness, with industrial production down by -1.9% on the
month in April, whilst wider regional exports slid by -2.5%.
Eurozone growth therefore remains primarily dependent on private
consumption, supported by the positive tailwinds of low
unemployment and higher wage compensation. This is likely to help
ensure further, albeit similarly subdued, GDP growth in the third
quarter of the year. Our initial nowcast for Q3 2019 is for 0.18%
q/q GDP expansion.
The picture is similar in the UK, albeit a little more volatile
in terms of the growth profile following the strong boost to
national output in the first quarter from Brexit-related
stockpiling in the run-up to the original EU exit date of
29th March.
Following the release of poor April industrial output numbers
(down -2.7% on the month) and trade data (exports down -2.0%,
imports down -6.3%), plus ongoing PMI softness up to June, our
nowcaster has been revised dramatically lower in recent weeks.
Indeed, the nowcast model suggests a slight contraction of the
UK economy (-0.06%) in the second quarter of the year, whilst an
initial nowcast for the third quarter points to economic stagnation
(0.04%).
Although a little early to raise the possibility of a UK
recession, if the newsflow continues to disappoint in the coming
weeks the chances of such an outcome will inevitably rise.
Summary
In this research paper we introduced new nowcasting models to
track economic growth in the eurozone and the UK. Drawing on a
wide-variety of indicators to track economic performance, we found
that these nowcasters leaned heavily on our own PMIs to provide
reliable and timely estimates of growth.
Going forward, we plan to continue to update and communicate our
nowcast results on a regular basis via our commentary webpage
at www.ihsmarkit.com and historical data are available to
subscribers on request. Any feedback from users is also
welcomed.
Finally, we are also developing similar models for other large
economies in Europe and around the globe. These will be introduced
- and updated on a continuous basis - later in 2019.
[1]
Note, however, that we stop short of replicating the data vintages
that would have been available at the time, prior to subsequent
revisions, due to difficulties in obtaining these data.
[2]
We discussed the importance of timeliness in an economic indicator
in our paper "Eurozone PMI and predicting economic growth. Please click here for full
report.
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.
