The China compass • We present two quantitative tools to help forecast China’s economy: a composite leading indicator (Nomura CLI) and a heat-map that tracks changes in 32 important headline and leading indicators in China. The two measures have been designed to complement each other in helping to navigate the direction of China’s economy. • With a six-month lead, the Nomura CLI has a stronger correlation with industrial production (IP) growth than China’s official PMI. The CLI’s turning points have an average two- month lead on IP growth turning points, while the official PMI’s turning points lag those of IP growth. The number of hot indicators identified on the heat-map also helps to forecast turning points in IP growth, which of all the core monthly data is the most correlated to quarterly GDP. • The latest readings show that, after reaching a bottom in November 2011, the Nomura CLI has risen for four straight months to March 2012, while the number of hot indicators in our heat map has also increased over this period. This is a signal that economic growth will likely rebound beyond Q1 2012, consistent with our H2 2012 GDP growth forecasts. I. Nomura’s China composite leading indicator 4 Comparing the Nomura CLI with other leading indicators 5 Caveat and remedies 6 Nomura CLI and equity and commodity prices 7 Methodology and components 8 II. Nomura’s China heat-map 10 III. Heat-map indicators 13 GLOBAL ECONOMICS AND STRATEGY April 2012 ANCHOR REPORT China economy See Appendix A-1 for analyst certification, important disclosures and the status of non-US analysts. April 23, 2012 Research analysts Economists Zhiwei Zhang [email protected]+852 2536 7433 Wendy Chen [email protected]+86 21 6193 7237
China‟s economy is now the world‟s second-largest, contributing over two-fifths of global GDP growth last year – yet the availability, and many would argue, the quality, of its economic statistics is lagging. It is difficult to get a clear read on the pulse of China‟s rapidly transforming economy, let alone its outlook. Just last month, the official PMI and an alternative private-sector measure parted ways. To help fill this void, we are pleased to present Nomura‟s latest Anchor Report, The China compass, in which we present two proprietary quantitative tools that we developed to help navigate China‟s economy: Nomura‟s composite leading indicator (Nomura CLI) and a heat-map that tracks changes in 32 important headline and leading economic indicators in China. Our two measures complement each other, and after considerable back-testing we are confident that they will prove useful additions to your tool-kit in helping to forecast turning points in China‟s economy. We shall provide an update of The China compass each month. This report builds on Nomura‟s continued effort to provide our clients with cutting-edge research. In terms of our China coverage, it follows November 2011‟s Anchor Report on China risks, in which we presented our China Stress Index (CSI), developed to assess the risk of a hard economic landing. As ever, we welcome your feedback as we continue our tradition of delivering unique investment insight and ideas to our clients. Hideyuki Takahashi Head of Global Research
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The China compass • We present two quantitative tools to help forecast China’s
economy: a composite leading indicator (Nomura CLI) and a heat-map that tracks changes in 32 important headline and leading indicators in China. The two measures have been designed to complement each other in helping to navigate the direction of China’s economy.
• With a six-month lead, the Nomura CLI has a stronger correlation with industrial production (IP) growth than China’s
official PMI. The CLI’s turning points have an average two-month lead on IP growth turning points, while the official PMI’s turning points lag those of IP growth. The number of hot indicators identified on the heat-map also helps to forecast turning points in IP growth, which of all the core monthly data is the most correlated to quarterly GDP.
• The latest readings show that, after reaching a bottom in November 2011, the Nomura CLI has risen for four straight months to March 2012, while the number of hot indicators in our heat map has also increased over this period. This is a signal that economic growth will likely rebound beyond Q1 2012, consistent with our H2 2012 GDP growth forecasts.
I. Nomura’s China composite leading indicator 4
Comparing the Nomura CLI with other
leading indicators 5
Caveat and remedies 6
Nomura CLI and equity and commodity prices 7
Methodology and components 8
II. Nomura’s China heat-map 10
III. Heat-map indicators 13
GLOBAL ECONOMICS AND
STRATEGY
Apr i l 2012
AN
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OR
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PO
RT China economy
See Appendix A-1 for analyst certification, important disclosures and the status of non-US analysts.
Which indicators best forecast China‟s economy? This is one of the most frequently
asked questions as people are bombarded by anecdotes on the health of China‟s
economy, with a swathe of partial indicators from which to choose, ranging from
electricity consumption to port container traffic. Yet, these data are narrow and not
always reliable. China's economy has become so critically important for the global
economy, and yet, a composite leading economic index that is both timely and reliable,
to help objectively forecast China‟s economic outlook, has been lacking.
Our composite leading indicator (Nomura CLI) of China's economic cycle is designed
to help identify turning points in advance. We reviewed a broad range of 52 monthly
indicators for their leading properties, quality and timeliness before choosing the best
nine to formulate the Nomura CLI. These nine indicators are: quasi money; the OECD
leading indicator for developed and major emerging economies (OECD total); yield
spread between 3-year and 6-month government securities; Shanghai stock market
turnover; gap between producer‟s output prices and input prices; and the production of
steel, automobiles, chemical fiber and metal cutting machinery. More details of these
indicators and the methodology used in constructing the index are explained on page
8. We will update the Nomura CLI on a monthly basis, incorporating all information
available by the release date.
The Nomura CLI picked up in December 2011 and over the first three months of 2012,
after trending down since November 2010 (Figure 1). Historical data indicate the
turning points of our CLI leads those of industrial production growth by an average of
two months over the past 10 years. Based on the Nomura CLI, we conclude that
economic growth bottomed in Q1 and is now more likely to rebound than decline. The
reversal in the CLI supports our forecast of 8.4% GDP growth for 2012.
With respect to which factors caused the Nomura CLI to rise recently, of the nine
components, five have risen since November and four declined (Figure 2). Quasi
money jumped partly due to monetary policy loosening. Stock market turnover picked
up, which reflects improved sentiment due to policy easing, and may help
consumption through positive wealth and confidence effects. The gap between
producers‟ output and input prices rose slightly, improving corporate profit margins.
Steel output improved marginally. The OECD leading indicator for developed and
major emerging economies rose, which suggests that external demand is improving.
On the downside, production of autos, chemical fiber, and metal cutting machinery
continued to weaken, while the yield spread narrowed.
Fig. 1: Nomura CLI and industrial production growth
Source: CEIC and Nomura Global Economics.
Fig. 2: Changes of components of Nomura CLI from November 2011 to March 2012
Note: „Machine‟ refers to metal cutting machinery. Source: CEIC and Nomura Global Economics.
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PMI sometimes gave right signals but is not consistent OECD China leading indicator is released with a two-month lag We launch our Nomura China leading indicator in this report
The Nomura CLI has risen since December 2011…
… as five of nine components have been positive since December
Investors need reliable and timely leading indicators for China
Nomura | Asia Special Report 23 April 2012
5
Comparing the Nomura CLI with other leading indicators
China‟s official Purchasing Managers‟ Index (PMI) arguably gets the most market
attention as a leading indicator for China. While it is timely and has worked well, it is
volatile and has also at times given the wrong signal. For example, in 2007-08, GDP
and industrial output data indicated that economic momentum peaked in June 2007
and trended down in 2008, but the PMI rose in H1 2008 and reached its highest level
in April 2008 (59.2) before plummeting sharply to 38.8 in November 2008 (Figure 3).
Also, in 2009-10, industrial output growth peaked in November 2009 but the PMI
continued to rise until January 2010.
The OECD‟s China CLI has worked well in forecasting business cycles. It has
successfully identified, in advance, all the turning points in China‟s industrial output
growth since 1979. However, a big drawback is that it is released with a considerable
two-month lag (the latest reading released on 10 April reflects data up to February
only), and therefore does not get as much market attention as the official PMI (more
discussion on pages 30 and 33).
The Nomura CLI, when back-tested, works well in forecasting industrial output growth
over the past 10 years. Compared to the official PMI, the Nomura CLI has had a
stronger correlation with industrial output growth since 2005. With a one-month lead,
the correlation coefficient between the Nomura CLI and industrial output growth is 0.70,
while the correlation coefficient between the official PMI and industrial output growth is
0.55. This difference is persistent over longer forecast horizons as well. With a six-
month lead, the correlation coefficient between the Nomura CLI and IP growth is 0.69,
while it is 0.56 between the PMI and IP growth.
In terms of indentifying turning points, the Nomura CLI also performs well (Figure 4).
There have been six distinct turning points (three peaks and three troughs) in China‟s
industrial output growth over the past 10 years. Our CLI correctly signals all six turning
points with an average two-month lead, compared to a one-month lead of the OECD
China CLI. In fact, since 2005 turning points in the official PMI actually lag turning
points in industrial output by an average of 3.7 months.
The Nomura CLI performs better than the PMI in identifying turning points in the
business cycle because the two indicators are designed for different purposes. The
PMI is designed to measure the change of economic momentum on a month-to-month
basis. For instance, the PMI new order index shows how many firms received more
orders this month compared to the last. The answer can be volatile and highly
seasonal. The Nomura CLI, on the other hand, is designed to forecast turning points in
business cycles and all components are selected based on their forecasting ability.
Fig. 3: PMI and industrial production growth
Source: OECD, CEIC and Nomura Global Economics.
Fig. 4: Peaks and troughs of Nomura CLI, OECD CLI, official PMI and IP growth
Source: CEIC and Nomura Global Economics.
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IP growth OECD CLI Official PMI Nomura CLI
Trough Nov-01 Nov-01 Sep-01
Peak Feb-04 Jan-04 Jan-04
Trough Feb-05 Mar-05 Jul-05 Dec-04
Peak Jun-07 Sep-07 Sep-07 May-07
Trough Nov-08 Nov-08 Nov-08 Nov-08
Peak Nov-09 Dec-09 Dec-09 Sep-09
Trough Sep-11 Nov-11 Nov-11
Turning points of the Nomura CLI precede those of China‟s economy by an average of two months
The official PMI does not consistently provide correct signals
The OECD China leading indicator is released with a two-month lag
Nomura CLI forecasts China‟s economy well
Nomura | Asia Special Report 23 April 2012
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Caveat and remedies
Leading indicators based on statistical tools often face an end-point problem: the
values (particularly for those months that are close to the end of the sample) are
subject to revision when new data becomes available. For instance, the Nomura CLI
did not clearly identify November 2008 as a trough until data for February 2009 was
made available, because the value of the CLI for December 2008 was revised when
new data became available (Figure 5). The OECD China leading indicator is also
subject to this problem. Its release in March shows the leading indicator trended down,
while its release on 10 April shows the leading indicator was revised and a bottom in
September 2011 was identified.
This issue is problematic, but unfortunately also unavoidable. It is a common problem
in econometric analysis. An intuitive way to think about it is that underlying data are
volatile; hence, we need to have several months of observations to confirm whether
the change in the index is the beginning of a genuine upturn or downturn. To be
specific and somewhat technical, we need to apply statistical filters to underlying data,
and the filtered results are not always highly robust at the end of the sample.
How much does this problem affect the Nomura CLI‟s forecasts in practice? One way
to address this question is to calculate for each turning point in the Nomura CLI how
many months after the turning point is first observed until we are reasonably confident
that it is genuine. To do this we conducted a back-testing exercise, and conclude that
a turning point can be confirmed, on average, 2.3 months after it is first seen. Of the
six turning points, in the best case the turning point was detected immediately after the
data for the next month was made available (September 2001); in the worst case, it
took four months to confirm the December 2004 trough.
Despite the end-point problem, the Nomura CLI is useful for economic forecasting in
real time. In March 2012, for example, the official PMI and the HSBC PMI moved in
opposite directions and there was a high level of uncertainty in the market in whether
growth bottomed in Q1. Around that time (mid-March), the latest data point for the
Nomura CLI was February, which had identified a bottom in November 2011. The
Nomura CLI turning point prediction was a robust signal given that it had risen for
three straight months (more than the 2.3 month average) after the turning point, and
therefore useful for investors to draw an inference on the likelihood of an economic
rebound ahead. On 10 April, the OECD China leading indicator also identified a
bottom in Q4 2011, which is consistent with the signal from our CLI.
Fig. 5: Revision of the Nomura CLI in 2009
Source: CEIC and Nomura Global Economics.
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One problem of the Nomura CLI is that forecasts are subject to revisions when new data arrive
Turning points are confirmed usually two months after they take place
Nonetheless, the Nomura CLI is still useful for economic forecasting in real time
Nomura | Asia Special Report 23 April 2012
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Nomura CLI and equity and commodity prices
The Nomura CLI has been strongly correlated with the Shanghai stock market index
since 2005 (Figure 6) and has led it in some periods. In 2007, the Nomura CLI peaked
in May while the Shanghai index peaked in October. In 2008, both the Nomura CLI
and the Shanghai index bottomed in November. In 2009, the Shanghai index peaked
in August while the Nomura CLI peaked in September. In the most recent cycle, the
Nomura CLI bottomed in December 2011 while the Shanghai index bottomed on 5
January 2012.
The Nomura CLI has also led copper futures prices in Shanghai on several occasions.
The leading index peaked in May 2007, while copper futures were range-bound for
most of 2007 and dropped sharply in 2008. The leading index rebounded in December
2008, while copper futures bottomed in January 2009. The turning points of the
leading index in 2009 and 2010 also preceded turning points in copper prices.
We think the high contemporaneous correlation between our leading indicator and
asset prices is primarily due to the fact that asset prices are forward-looking and
incorporate important events, such as policy changes, in a timely manner. In late 2008,
China implemented its RMB4trn fiscal stimulus program which pushed up both the
Shanghai index and economic activity. Hence the rebound of these two indicators was
synchronized. In August 2009, monetary policy was adjusted from a super-loose
stance to one that was more neutral. The equity market responded quickly, while
economic activity started to weaken with a slight lag. In December 2011, monetary
policy was loosened and the market rebounded in response. Again, economic activity
responded with a lag to policy loosening as the transmission of policy to economic
activity took time.
Part of the correlation between the Nomura CLI and the Shanghai index is due to the
utilisation of stock market turnover as a component. However, we do not believe this is
the key reason, because: 1) it is only has an 11% weighting in the Nomura CLI; and 2)
it does not explain why the Nomura CLI led the collapse of the Shanghai index in
October 2007.
Fig. 6: Nomura CLI and the Shanghai A-share index
Source: CEIC and Nomura Global Economics.
Fig. 7: Nomura CLI and copper futures prices
Source: CEIC and Nomura Global Economics.
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Stock prices are forward-looking and reflect policy changes that drive both the CLI and stock index
The Nomura CLI has also led copper futures prices
Nomura | Asia Special Report 23 April 2012
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Methodology and components
We chose China‟s industrial production growth as our reference series (i.e., the data
we attempt to forecast). Industrial production was chosen instead of GDP for three
reasons. First, industrial production is a monthly indicator that helps provide a more
timely measure of the economy, while GDP is only available quarterly. Second,
industrial production growth is a good proxy for GDP growth as the two are highly
correlated (Figure 8). Third, industrial production growth has worked well in the past as
a coincident indicator of business cycles. For example, it dropped sharply after the US
financial crisis escalated in September 2008 and rebounded quickly in early 2009 after
China implemented its fiscal stimulus program.
The Nomura CLI is constructed as a composite of nine leading indicators, each given
an equal weighting. These were selected from a large pool of monthly variables, many
of which are discussed in the “Heat-map indicators” section of this report. The nine
indicators are:
1. Quasi money. Quasi money refers to assets that can be readily converted
into cash. In China, it is mainly made up of demand deposits. It is a measure
of liquidity, with stronger leading properties than others such as M2 (Figure 9).
This indicator is influenced by both monetary policy as well as firms‟ and
households‟ decision to allocate assets.
2. OECD total leading indicator. This is the OECD‟s aggregate leading
indicator for all OECD countries and six emerging markets combined. It is a
measure of external demand, which leads China‟s export growth well (Figure
10).
3. Steel production. Steel is a highly cyclical sector and is highly dependent
upon housing construction (about 30% of demand goes directly into the
housing sector). It is closely watched by investors as a barometer of
economic momentum.
4. Automobile production. The automobile sector has quickly become an
important sector of China‟s economy.
5. The differential between the producers’ ex-factory price index and the
input price index. This measure indicates how profit margins are affected by
costs (input prices) and market demand (ex-factory prices). It influences both
profitability (Figure 11) and investment decisions, and hence leads IP growth.
6. Shanghai stock market turnover. This is a measure of market sentiment,
which sometimes works well in reflecting changes in policy. We also found
that stock market turnover is a better leading indicator of IP growth than stock
indexes.
7. Chemical fiber production. Chemical fiber is extensively used as an input
for industrial production and is highly sensitive to both demand- and supply-
side factors such as oil prices.
Fig. 8: Quarterly growth of industrial production and GDP
Source: CEIC and Nomura Global Economics.
Fig. 9: Growth of quasi money, M2, and industrial production
Source: CEIC and Nomura Global Economics.
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We choose to forecast IP growth because it is available monthly and tracks GDP well
We have selected nine indicators as components of the Nomura CLI
Nomura | Asia Special Report 23 April 2012
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8. Production of metal cutting machinery. This is an upstream indicator of
metal production.
9. The differential between 3-year government bond yields and 6-month
government bill yields. This is a proxy for monetary policy and market
liquidity.
More details of these variables, such as historical data and correlations with IP growth,
are provided in the “Nomura‟s China heat-map” and “Heat-map indicators” sections.
We used four criteria in choosing our nine component indicators.
1. Data must be released on a monthly basis and must have been
available for more than five years so that a proper statistical analysis is
feasible. Indeed, for eight of the nine variables, there is more than 12 years of
data available so we can test how the composite indicator has performed
over the past decade.
2. We favour data without measurement problems. For instance, the
housing sector is very important, but many housing data have measurement
issues that make statistical analysis difficult, so we chose steel production
instead. Steel production depends heavily on housing yet is superior to
housing indicators in forecasting industrial production growth. We also try to
avoid data with strong price effects that are hard to control.
3. From a statistical perspective, all the inputs must be good leading
indicators of industrial production growth. To check this, we ran simple
correlation tests with different lead months over a large pool of indicators and
filtered out all but those with the highest correlations.
4. The variables must complement each other. A horse race among
indicators helps to rank them in terms of individual predictive power, but a
combination of the best individual indicators does not give the best composite
indicator, so we tested different combinations to determine the mix of
variables that gave the best collective forecast.
We seasonally adjust these variables, de-trend them using the Hodrick-Prescott filter
and then combine them in an equal weighting to form the Nomura CLI. We could have
assigned weightings subjectively to better fit with industrial production growth over the
past 10 years, but the difference is not very substantial and it will not necessarily
improve future forecast accuracy.
Fig. 10: OECD leading indicator and China’s export growth
Source: OECD, CEIC and Nomura Global Economics.
Fig. 11: Differential between producers’ ex-factory price index and input price index, and corporate profit growth
Note: Corporate profit was reported on a quarterly basis from 2007 to 2011, hence the gap in this period for some months in this chart. Source: CEIC and Nomura Global Economics.
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We select our indicators based on availability and their capacity to forecast IP growth…
… and give each an equal weighting in the Nomura CLI
Nomura | Asia Special Report 23 April 2012
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Nomura‟s China heat-map
Our heat-map gauges economic momentum across a wide range of the best macro
and micro indicators for China. We thoroughly examined 52 high-frequency indicators
that we think are valuable in forecasting China's economy, and filtered out all but 32
based on their ability to forecast industrial production growth. The final product is a
map that covers, in our view, all the useful leading indicators of China's economy,
including the widely watched PMI and OECD leading indicators, as well as lesser-
known variables such as the production of metal cutting machinery and chemical fibers.
The heat-map is designed to supplement the Nomura CLI in economic analysis –
consider it a kind of robustness check. The Nomura CLI comprises only nine indicators
and is compiled following a statistical procedure based on in-sample forecasts. The
heat-map, on the other hand, includes 32 indicators, covering investment, consumption,
foreign trade, monetary policy, financial market indicators and select micro-level
indicators. The purpose of the Nomura CLI is to forecast economic turning points, while
the purpose of the heat-map is to offer readers a more comprehensive view on the
positive and negative drivers of the economy, supported by a wide range of data.
The heat-map provides a useful signal for turning points in economic cycles – the
number of “hot” indicators, defined as data where the year-on-year growth rate has
increased from the previous month, is historically volatile, but a significant change in
this number around the time of a potential economic turning point can help to validate
whether a turning point has actually been reached (Figure 12). For example:
In December 2008, the number of hot indicators rose to 13 from 8 in
November on a 3-month-moving-average (3mma) basis. Note that industrial
production growth was still in its downtrend in December, the PMI was at 42 –
well below the 50 threshold. In January 2009, when these data were available,
there were serious concerns over China‟s economic outlook. The Shanghai A
share index rose from 2,089 at end-January 2009 when all December data
were released to 3,582 by July 2009.
In July 2009, the number of hot indicators on a 3-month moving average
(3mma) basis dropped to 14.7 from 16 in June. This was six months before IP
growth peaked in January 2010, four months before the PMI peaked in
November 2009, and two months before Nomura CLI peaked in September
2009. When the number of hot indicators declined, the Shanghai A share
index ended its seven-month rally and fell by 22% in August 2009.
The number of hot indicators can help to cross-check the turning points identified by
the Nomura CLI (Figure 13). It is particularly useful because it is much less subject to
revision when new data are released. For example, the turning point of the Nomura CLI
in November 2008 could not be confirmed until March 2009, when data for February
2009 was made available, while the number of hot indicators picked up in December
which helped to confirm that momentum had indeed picked up.
The latest reading of the heat-map shows tentative signs of an economic rebound, with
the number of hot indicators rising from 11 in November to 14 in March 2012, and from
12 to 15 on a 3mma basis.
Fig. 12: IP growth and number of hot indictors
Source: CEIC and Nomura Global Economics.
Fig. 13: Nomura CLI and number of hot indicators
Source: Nomura Global Economics.
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Nomura | Asia Special Report 23 April 2012
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Fig. 14: China heat-map
Notes: Indicators with an * are indexed, while the rest are y-o-y growth numbers. Source: CEIC and Nomura Global Economics.
y-o-y growth, unless otherwise mentionedLeading
MonthNov-11 Dec-11 Jan-12 Feb-12 Mar-12
Industrial production 12.40 12.80 11.40 11.40 11.90
Investment
FAI (ytd) 1 27.89 25.07 21.47 21.47 21.29
Real Estate Investment 1 20.15 12.33 27.79 27.79 19.58
New floor space started 1 9.09 -18.85 5.06 5.06 -4.15
Production: Chemical fibre 1 5.31 -0.40 17.67 17.67 12.55
Production: Ten non-ferrous metals 1 5.44 8.45 9.50 9.50 1.49
Production: Cement 3 6.50 3.19 5.67 5.67 8.95
Production: Cloth 1 -3.60 -8.26 9.50 9.50 6.92
Production: Yarn 2 11.57 8.11 12.78 12.78 20.55
Production: Chemical fertilizers 5 -11.56 -13.74 15.05 15.05 15.81
Production: Metal cutting machine 0 -5.63 -8.45 11.00 11.00 2.39
No of indicator with accelerated growth 11 13 19 11 14
Nomura | Asia Special Report 23 April 2012
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Selecting data for the heat-map
In creating the heat-map, we started by scanning all available high frequency
indicators (monthly, weekly and daily) and select 52 indicators that we think may be
useful for either representing an important part of the economy (fixed asset investment
is one example), or forecasting economic growth (PMI is included for this purpose).
This set of variables includes eight indicators for investment, seven for consumption,
six for foreign trade, six financial and monetary indicators, eight survey and third party
indicators, 16 selected micro level indicators and one price data point on steel. We
preferred variables with at least 10 years of historical data in order to properly perform
a statistical test of their forecasting ability, but allowed exceptions, such as PMI data
which has only been available since 2005.
From this sample, we carried out a statistical procedure to select a group of 32
variables that were highly correlated with IP growth or enjoyed a long lead time. For
macro and financial indicators we dropped those with a correlation of less than 0.2
(the two exceptions are freight carried, and gap between output and input price index).
For micro level indicators that are subcomponents of overall industrial production (for
instance steel production), we excluded those leading indicators with a correlation of
less than 0.35. After this procedure, 32 indicators remained in our pool.
Fig. 15: Correlation table
Notes: Indicators with an * are indexed, while the rest are y-o-y growth numbers. The correlations in this sheet are calculated with data from Jan 2000, adjusting for Chinese New Year effects by averaging growth for Jan-Feb.
Source: CEIC, WIND and Nomura Global Economics.
Correlation
Leading
month Correlation
Leading
month
Investment Survey and third party indicators
FAI (ytd) 0.46 1 ECI leading index* 0.59 1
Real estate investment growth 0.42 1 OECD CLI* 0.70 2
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