Download `N Sync: how do countries` economies move together?

‘N Sync: how do countries’ economies move
together?
AN 2014/04
James Graham
August 2014
Reserve Bank of New Zealand Analytical Note series
ISSN 2230-5505
Reserve Bank of New Zealand
PO Box 2498
Wellington
NEW ZEALAND
www.rbnz.govt.nz
The Analytical Note series encompasses a range of types of
background papers prepared by Reserve Bank staff. Unless otherwise
stated, views expressed are those of the authors, and do not
necessarily represent the views of the Reserve Bank.
Reserve Bank of New Zealand Analytical Note Series
2
Non-technical summary
Connections between short-term economic developments in one country and those in other countries received renewed attention following the global recession of 2008-2009. For a small country such as New
Zealand, reliant on commodity exports and substantial amounts of offshore debt, the connections are of
particular interest. This paper takes a look at some of those connections over the period since 1990. Using
data for 22 countries across a range of key economic variables, several statistical techniques are used to
explore the extent, and nature, of any short-term connections.
In the first part of the paper, a common element across all of the countries’ data is identified and is treated as
measure of the global business cycle. Across these variables and over the sample period studied, around
30 percent of the variability in the countries’ same-quarter data can be explained by variability in this global
business cycle measure. New Zealand appears to be an outlier: only 6 percent of the variability in GDP can
be explained by the global business cycle, and just 3 percent of the variability in the value of exports. These
results may be due, in part, to the importance of agricultural exports.
The second part of the paper looks at which groups of countries have had more similar fluctuations in
economic activity over the period since 1990. This analysis takes into account countries’ responses to the
global business cycle as well as other patterns in the data. The countries are found to separate into two
broad clusters: Western and Asian countries. New Zealand’s economic fluctuations over this period have
been most similar to those of Australia, and both have been within the Western country cluster.
The third part of the paper employs a technique previously used to study the diffusion of popular music
listening trends. Here, the technique is used to identify the flow of business cycle fluctuations between
individual “leader” and “follower” countries. Abstracting from the influence of the global business cycle, only
the country-specific – or idiosyncratic – relationships between countries’ business cycles are considered.
Illustrated by a network diagram, some countries are identified as strong leaders, such as Korea, Malaysia,
and the United States, while others are strong followers, such as South Africa, the Philippines, and New
Zealand. On this measure, most Asian countries appear to have been more connected and to have had more
influence on other countries than the Western economies. However, China shows up as a strong follower of
fluctuations in Western countries, and a weak leader of Asian economies, reflecting its relationship over this
period to Western demand fluctuations and Asian manufacturing supply chains.
For New Zealand, little of the current quarter’s fluctuations in economic activity is explained by the current
quarter measure of the global business cycle. But once lagged effects are allowed for, New Zealand’s
fluctuations are substantially explained by earlier fluctuations in specific other countries (as distinct from the
global business cycle itself). To the extent that New Zealand is connected to other countries, it has tended
to be a follower rather than a leader, as we might expect.
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1 Introduction
New Zealand’s short-term economic fortunes are often regarded as being quite heavily influenced by international developments. Indeed, Reddell and Sleeman (2008) argued that each major economic downturn
in New Zealand since at least the 1930s seems to have been triggered in significant part by international
events. And yet, while New Zealand has a relatively open economy, it may not be as strongly connected
to the international economy as other countries (Treasury, 2009). One way to empirically assess New
Zealand’s relationship with the rest of the world is by comparing its business cycle fluctuations to other
countries’ business cycle fluctuations. For example, figure 1 shows annual average GDP growth rates for
New Zealand and several of its more important trading partners.
This paper uses a range of statistical techniques to look more closely at the relationships and linkages
between the short-term economic performance of a range of countries. In particular, global and countryspecific business cycles are constructed using principal components analysis on 22 countries’ data for GDP,
consumption, investment, exports, industrial production, and employment. The similarities between countries’ business cycles are then assessed using a technique called hierarchical clustering analysis. Finally,
leader-follower relationships between countries are analysed using a VAR model, connectedness measures,
and network diagrams.
2 Other literature
The relationships between international business cycles have been much investigated in the literature. Early
studies considered international cross-correlations in business cycle variables, finding significant differences
between the cross-correlations implied by theory and those present in the data (Backus et al., 1992). Indeed,
Obstfeld and Rogoff (2001) cite the stark lack of co-movement in international consumption growth rates as
one of the ’Six Major Puzzles in International Macroeconomics’.
In more recent literature, authors have shifted the emphasis from theory to international business cycle data
itself. A popular approach focuses on the possibility that different countries’ business cycles are driven by
common global or regional factors. Several authors use dynamic factor models (DFMs) to estimate these
common factors and find that global factors explain a significant proportion of the variance in countries’
business cycles (see Crucini et al., 2011; Kose et al., 2012, 2003).
Other authors have used the global vector auto-regression framework, which adapts the simple VAR model
to allow large panels of data without placing onerous restrictions on the estimation procedure (GreenwoodNimmo et al., 2012).
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Figure 1: Real GDP growth rates
Some authors have tried to identify the specific economic shocks that influence international business cycles
using structural VAR models (Ahmed et al., 1993; Stock and Watson, 2005). And others have returned to
the theory-first approach by exploring international business cycles using structural DSGE modeling and
estimation (Justiniano and Preston, 2010).
Many of these modeling frameworks entail difficult choices about the model restrictions required to identify
particular economic models, shocks, or factors. But there are simpler, alternative approaches. For example,
the econo-physics literature uses lagged cross-correlations and principal components analysis to explore
the relationship between countries’ business cycles via network theory (Ausloos and Lambiotte, 2007; Gligor
and Ausloos, 2007; Miśkiewicz and Ausloos, 2006; Redelico et al., 2009). Diebold and Yılmaz (2013) take
a similar but more econometric approach by combining simple vector error correction models (VECMs) with
the concepts of network theory to assess the relationships between international business cycles.
This paper uses these relatively simple techniques, but takes inspiration from two studies from outside the
economics literature: one studying the geographic flow of music listening trends (Lee and Cunningham,
2012), and the other studying the dynamics of pigeon flock leadership (Nagy et al., 2010).
3 Data
To assess the relationship between international business cycles, this paper uses data for New Zealand, its
main trading partners, and several other large economies. Table 1 lists the countries analysed and reports
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the average of their merchandise trade shares over the period 2000M1:2014M5.1
Discussion of business cycles often refers simply to fluctuations in real GDP. However, variations in GDP do
not necessarily capture the common movements across macroeconomic variables. The NBER, for example,
considers several variables in dating US business cycles: real GDP, employment, real income, retail sales,
and industrial production.2 Others have considered variables such as GDP, investment, and consumption
(King et al., 1992; Kose et al., 2012), and GDP and unemployment (Blanchard and Quah, 1990).
This paper uses a broad range of business cycle-related variables: real expenditure GDP (Yt ), real household consumption expenditure (Ct ), real gross fixed capital formation (It ), real manufacturing industrial production (IPt ), nominal merchandise exports(Xt ), and the number of employed persons (Et ).34 The data
capture many of the variables in the analysis mentioned above, and also account for fluctuations in the tradable sector via the value of exports, which are likely to be an important part of the business cycle for New
Zealand and many of its open economy trading partners.
All data are taken from Haver. Most countries have data available from 2000Q1, and many have a considerable amount of the data available from 1990Q1. A table describing the series and their availability for each
country is provided in the Appendix. The series are seasonally adjusted and observed at or converted to
a quarterly frequency. The data are log-differenced to represent percentage growth rates for each variable,
and are also demeaned and divided by their standard deviation. Demeaning and standardising corrects
for the fact that quarterly fluctuations in some variables are much larger than others, such as investment
relative to consumption. For reference, the New Zealand data, as transformed, are shown in figure 7 in the
Appendix.
The primary method used in this paper is principal components analysis. As this method allows for missing
data, the sample period is from 1990Q1 to 2013Q4.5
1 India
is excluded from this analysis despite a trade weight of 1.3 percent because we lack Indian data for many of the relevant economic
time series. Large economies like South Africa and Russia are also interesting because they are commodity exporting countries like
New Zealand.
2 See the NBER’s Business Cycle Dating Committee for details.
3 Nominal merchandise exports are used rather than real exports of goods and services because the former is available over a longer
period than the latter for many countries. Additionally, nominal exports has the advantage of capturing the considerable variability in
the terms of trade that has affected countries like New Zealand and Australia. For New Zealand, most of this volatility has appeared in
world export prices rather than imports, as noted in Steenkamp (2014).
4 For New Zealand, Haver’s industrial production series is the goods component of GDP, which includes manufacturing, construction,
and electricity, gas, and water. This means that New Zealand’s industrial production series includes primary food manufacturing.
5 In order to account for missing data, principal components analysis is run on the covariance matrix of the data. If data is missing for any
particular variable, the covariance between it and any other variable is simply based on a shorter time series than for two variables with
all data available. Once principal components coefficients are estimated, the principal components themselves can be reconstructed
by setting the observations for missing data equal to their mean of zero.
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Table 1: New Zealand average trade shares since 2000
Australia
US
China
Japan
UK
Germany
Korea
Singapore
Malaysia
Taiwan
Thailand
20.2
11.6
10.4
9.3
3.6
3.4
3.3
2.6
2.5
2.0
1.9
France
Indonesia
Italy
Canada
Hong Kong
Philippines
Spain
Russia
South Africa
Brazil
1.7
1.7
1.7
1.5
1.1
0.9
0.6
0.5
0.4
0.3
Trade shares are calculated as the the share of New Zealand’s total merchandise exports and imports for a given trading partner. The values reported are
the average over the period from 2000M1 to 2014M5.
4 Estimating the global business cycle
In order to approximate global and country-specific business cycles, principal components analysis is conducted using all six business cycle variables.
While some studies consider several business cycle variables individually (e.g. Kose et al., 2003), others
have considered several variables simultaneously in order to take into account common movements across
those variables (e.g. Crucini et al., 2011; Kose et al., 2012). As Kose et al. (2012) imply, the joint variable
analysis accounts for the effects of many types of shocks, which may affect the variables in similar ways.
4.1 Principal components analysis
The principal components analysis proceeds in three steps. As an initial step, the first principal component
is extracted from the entire data set. This first component is assumed to represent the global business cycle.
Second, the effect of the global business cycle is removed from every variable. Third, the adjusted variables
are grouped by country, and the first principal component is extracted from each country’s variables. These
first components are assumed to represent the country-specific business cycles i.e. country business cycles
abstracting from the effect of the global cycle.6
The assumption that the first principal component from the entire data set represents the global business
cycle can be justified in two ways. First, by construction the first principal component explains the greatest
share of the variance in the data. In this case, the first principal component explains 26 percent of the total
variance of the demeaned data across a wide range of countries.
6I
also tried two other methods for constructing the global business cycle. In the first method, I grouped variables by country, then
extracted the first principal component, i.e. the country business cycle including the effect of any global cycle. I then take the first
principal component out of the group of all country business cycles. I assume that this is the global business cycle. In the second
method, I estimated a small dynamic factor model with a global factor using code from Koop and Korobilis (2014). The global business
cycles produced under all three methods are very similar, so I proceed with the most straightforward method, described in the text.
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Figure 2: “Global business cycle”: first principal component from all data
Second, the first principal component seems to capture many significant global economic events. Figure 2
shows the evolution of the first principal component. Periods of weakness in this component coincide with:
the recession of the early 1990s, the Asian crisis from 1997, the dot-com bubble collapse and September
11 terrorist attacks of the early 2000s, the SARS epidemic in 2003, and the Global Financial Crisis (GFC) in
2008. There is also some indication of a steady increase in global activity in the mid-2000s preceding the
GFC.
The robustness of the principal components method for constructing the global business cycle is checked
by re-running the analysis using each of the business cycle variables individually. Figure 6 in the appendix
shows the global cycle estimated for each variable on its own, with the proportion of each variable’s variance
explained by that global cycle reported in the graph.
4.2 Explanatory power of the global business cycle
Table 2 shows how much of the variance of each variable is explained by the overall global cycle illustrated
in figure 2.
Exports and industrial production have the strongest average connection to the global cycle. On average 40
percent of the short-term variability in each countries’ exports is explained by same-quarter movements in
the common global cycle. The strong correlation of exports and industrial production with the global business
cycle is unsurprising. The ability to sell goods depends on the ability and willingness of someone else to
buy them, hence exports are directly linked to the economic fortunes of importing countries. And industrial
production constitutes many of the inputs that go into exported goods. Additionally, the strong co-movement
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Table 2: Percentage of variable variance explained by global business cycle
NewZealand
Australia
SouthAfrica
UnitedStates
Canada
Brazil
UK
France
Germany
Italy
Spain
Russia
Japan
Korea
Taiwan
HongKong
Singapore
China
Malaysia
Indonesia
Thailand
Philippines
Mean
Y
6
5
27
34
36
33
43
49
34
49
28
57
30
19
28
34
28
9
32
3
16
16
28
C
15
17
24
20
19
8
12
6
0
11
16
25
3
16
5
12
14
4
13
5
11
5
12
I
6
8
5
31
41
39
12
34
17
22
21
40
11
8
29
7
1
1
8
5
10
9
17
X
3
31
36
62
62
20
41
35
33
47
34
35
50
45
54
36
53
38
45
38
38
34
40
IP
25
23
35
55
33
15
54
64
58
57
46
45
57
33
21
12
11
24
43
6
14
21
34
E
10
12
0
32
31
11
12
15
1
7
33
21
3
7
16
4
6
0
0
–
0
0
11
Mean
11
16
21
39
37
21
29
34
24
32
30
37
26
21
26
18
19
13
24
11
15
14
in industrial production is likely reinforced by the increasing integration of global supply chains.
GDP is also relatively well explained by the global business cycle. For the average country, 28 percent of
the variability in output is explained. This finding is higher than the findings of much of the dynamic factor
model literature, which suggest that the explanatory power of the global factor is between 6 and 15 percent
(e.g. Kose et al., 2012, 2003).7 However, when only the G7 countries are considered, the common factor
has a much higher degree of explanatory power, at between 25 and 47 percent (e.g. Crucini et al., 2011;
Kose et al., 2008).8
In contrast, consumption and investment are relatively poorly explained by the global business cycle. For
the average country, only 12 and 17 percent, respectively, of the variability in consumption and investment
is explained by the global cycle. This is consistent with the findings in the dynamic factor model literature
when many countries are studied (e.g. Kose et al., 2012, 2003). This result for consumption is consistent
with the ‘consumption puzzle’ cited elsewhere in the literature (see Obstfeld and Rogoff, 2001).
Employment is not well explained by the global cycle. The reasons for this weak co-movement are not clear.
However, the implications of the quite different labour market and income support policies across countries
7 Kose
et al. (2003) study 60 countries using annual data from 1960 to 1999. Kose et al. (2003) study 76 countries using annual data
from 1981 to 1999. Kose et al. (2012) study 106 countries, using annual data from 1985 to 2005.
8 Kose et al. (2008) study G7 countries using quarterly data from 1986Q3 to 2003Q4. Crucini et al. (2011) study G7 countries using
annual data from 1960 to 2005.
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for the cyclical behaviour of total numbers employed may be part of the explanation. Note, too, that it is the
number of employed persons considered here, whereas the number of hours worked may have a tighter
correlation with the global cycle.
Across countries, developments in the large and economically developed countries tend to be best explained
by the global business cycle. For example, on average short term fluctuations in the G7 countries’ GDP are
39 percent explained by the global business cycle.9 This may seem unsurprising given the large influence
these countries have on global trade and economic conditions. However, it is worth emphasizing that these
results reflect contemporaneous co-movements between countries, rather than the lagged influences of
some countries on others.
New Zealand, China, and Indonesia had the weakest average connections to the global cycle (10, 13,
and 11 percent, respectively).10 For New Zealand, the global business cycle accounted for just 6 percent
of the variability in GDP, 15 percent of consumption, 6 percent of investment, and 3 percent of exports.
These results are similar to those of Kose et al. (2003), who find that only 11 percent of GDP, 9 percent of
consumption, and 8 percent of investment in New Zealand are explained by the global factor, while Kose
et al. (2012) find that only 8 percent of New Zealand’s GDP is explained by the global factor.
The lack of explanation of New Zealand’s exports is particularly stark. Part of the reason for this result may
be that New Zealand’s exports do not behave like other countries’ exports because of the high proportion
of agricultural products in New Zealand’s total exports. Agricultural production cannot respond as quickly to
changes in global demand as manufactured products can, for example, because biological constraints tend
to be binding over short horizons.
The lack of export co-movement may also be due to the impact of local weather patterns on New Zealand’s
agricultural exports, which may also account for the lack of connection between short-term fluctuations in
New Zealand’s GDP and the global cycle. Several papers confirm the importance of local weather patterns
on New Zealand’s economy. Buckle et al. (2002) employ a structural VAR model with quarterly data from
1983Q1 to 2002Q1 and find that climate shocks cause large movements in New Zealand’s exports and
GDP. They suggest that climate shocks are the dominant source of domestic shocks to the New Zealand
economy, with droughts having contributed significantly to the early 1990s and 1998 recessions in New
Zealand. Kamber et al. (2013) also employ a structural VAR model, using quarterly data from 1992Q1 to
2012Q4. They find that drought shocks have had a significant impact on exports and GDP, among other
variables, predicting, for example, that the drought in 2013 may have decreased annual GDP growth by
around half a percent.
9 This
compares with 25 percent in Kose et al. (2008) and 47 percent in Crucini et al. (2011). Kose et al. (2003) find that industrial
countries have much stronger connections to the global cycle than developing countries.
10 The results for China may be at least partly due to the nature of published Chinese statistics. Because Chinese GDP, consumption, and
investment are only available on an annual basis, these series have been interpolated to produce quarterly data. Additionally, Chinese
employment data is employment in urban units, which is only a subset of total employment. See the appendix for more details. Note,
however, that both exports and industrial production in China are reasonably well explained by the global business cycle (38 and 24
percent respectively).
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5 Hierarchical clustering analysis
As the principal components analysis shows, international business cycle movements appear to have a
significant common component. Several studies also consider whether there are regional or group factors
that affect countries’ business cycles. Kose et al. (2003) find that, with the possible exception of North
American region, there do not appear to be significant geographical business cycle factors. Kose et al.
(2012) consider countries grouped by their level of economic development and find that, in general, these
country groups also do not have large common business cycles.11
These studies consider whether pre-determined country groupings exhibit common business cycle movements. A non-deterministic method for exploring business cycle groupings is hierarchical clustering analysis,
which uses pairwise comparisons to group variables according to the degree of similarity between the observations for each variable. The method is common in the biological sciences and has been used recently
in the econo-physics literature (see Ausloos and Lambiotte, 2007; Gligor and Ausloos, 2007; Redelico et al.,
2009).
The similarity between variables can be determined in several ways. I use a principal components coefficients clustering algorithm (PCCCA), suggested by Gligor and Ausloos (2007). This algorithm groups
variables according to the signs of their principal components coefficients.12
A group of variables is more similar the greater the number of principal components coefficient signs they
have in common. The PCCCA calculates the distance (i.e. dissimilarity) between any two variables i and j,
where the value of the cth principal component coefficient for variable i is given by vi (c), and the distance
between i and j is given by d(i, j). Formally, the algorithm can be written as:
1. Set the principal component coefficient counter c = 1
2. Compare the signs of the principal component coefficients for variables i and j:
(a) If they are similarly signed, i.e. vi (c) × vj (c) > 0, go to step 3.
(b) If they are dissimilarly signed, i.e. vi (c) × vj (c) < 0, go to step 4.
3. Set d(i, j) =
1
c
and stop.
4. Set c = c + 1 and return to step 2.
Note that the distance between any two variables with differently signed coefficients declines non-linearly
as c increases. This is consistent with the fact that the proportion of the data’s variance explained by
11 However,
there is some suggestion that regional business cycles – small though they are – increased in importance over the latter part
of the 20th century.
12 For robustness I also calculated the similarity between country business cycles using the correlation between them. This produced
comparable results to those presented in the paper. However, as the correlation method only makes use of each country’s first principal
component (i.e. in constructing the business cycles), while the PCCCA uses all of the principal components coefficients, the PCCCA
is informationally superior.
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the principal components declines with each subsequent component. Hence, the difference between the
dissimilarity of variables whose coefficient signs diverge at c = 2 versus c = 3 is much larger than the
difference between the dissimilarity of variables whose coefficient signs diverge at c = 3 versus c = 4.
Once the distance for every variable pair, d(i, j), has been calculated, the simple average linkage clustering
algorithm groups variables according to the smallest distance between them.13
The hierarchical clustering analysis is conducted on two sets of variables. First, the first principal component
is extracted from each country’s data set, which produces a globally-unadjusted business cycle for each
country, and then principal components analysis is conducted on the set of country-specific business cycles.
The coefficients from this second-step principal components analysis can then be used to group countries
via the PCCCA.
Table 3 in the Appendix shows the first 10 principal components coefficients for each country. Again, the first
principal component is assumed to represent the global business cycle. Subsequent principal components
may represent common movements in the business cycles of particular country groupings.
Note that every country has a positively signed coefficient for the first principal component. This means
that every country’s business cycle moves with the global business cycle in the same way. For the second
principal component, Western countries, South Africa, and Russia have negatively signed coefficients, while
Asian countries and Brazil have positively signed coefficients. Thus these two groups of countries move
differently with the second common component of the data. For the third principal component, among the
Western countries, New Zealand, Australia, South Africa, the US, Canada, and the UK have positively
signed coefficients, while France, Germany, Italy, Spain, and Russia have negatively signed coefficients. In
this way, smaller and smaller country groups can be categorised with subsequent principal components.
Note that I do not attempt to give an economic interpretation to the principal components beyond the
first principal component. The focus here is simply on identifying the statistical similarities between comovements in international business cycles.
The second set of variables comes from principal components analysis conducted directly on individual
business cycle variables for every country. In order to represent these results on a single graph, the data set
cannot be very large. Hence, the variable set is restricted to GDP, industrial production, and exports. These
are variables with the most reliable data and longest sample size for most countries.
13 The
simple average linkage clustering algorithm is described in detail in Sibson (1973). The Matlab Statistical Toolbox contains several
different algorithms for grouping countries via hierarchical clustering analysis.
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5.1 Dendrograms
Hierarchical clustering analysis can be presented neatly in a graph known as a dendrogram. In the dendrogram, each country (or variable) is represented by a leaf in a tree. Similarities between countries are
indicated by pairings of leaves. Each pair can then be connected to other leaves or pairs by branches. The
fewer branch-connections between any two countries, the more similar those countries are. Additionally, the
lower is the height of a branch-connection between any two countries, the more similar those countries are.
Similar groups of countries are coloured for ease of identification.
The dendrogram for the hierarchical clustering analysis on the country business cycles is presented in
figure 3. The dendrogram for the hierarchical clustering analysis on individual business cycle variables is
presented in figure 4.
Figure 3: Dendrogram, 1990Q1:2013Q4
Figure 3 shows that the country business cycles fall into two broad clusters: Western countries plus Russia
and South Africa, and Asian countries plus Brazil. Within the Western country group, there are two distinct
sub-clusters. In the first sub-cluster, New Zealand, Australia, and South Africa are closely connected, as are
the UK, US, and Canada. In the other sub-cluster, Germany and France are the most similar, and are in fact
the most similar countries in the entire Western cluster.
Within the Asian country group, there are several sub-clusters. Advanced countries including Taiwan, Singapore, Korea, and Japan form one closely related group, while emerging countries including Malaysia, the
Philippines, Thailand, and China form another. Taiwan and Singapore are the most similar pair in the Asian
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cluster.
Figure 4 also suggests there are Western and Asian country clusters, and provides more detail about which
variables are driving that result and which variables provide exceptions to it. For the most part, Western
countries are clustered in red and green groups at the top of the dendrogram. There appear to be significant
similarities between the G7 countries, with 6 of the 7 industrial production variables and 5 of the 7 GDP
variables found within the red sub-cluster. The only non-Western variables included in the Western country
cluster are Japanese industrial production, which is closely related to the other G7 countries’ industrial
production and GDP, and Chinese and Philippines exports.
Asian countries are mostly clustered in the purple and blue groups at the bottom of the dendrogram. However, there also are several non-Asian country variables that feature in these groups. For example, the
purple sub-cluster is almost entirely constituted of export variables and includes Korea, Indonesia, Thailand,
Singapore, Malaysia, Japan, Spain, Russia, Australia, South Africa, and the UK. China’s GDP and industrial
production are also grouped with the the export variables in the purple sub-cluster.
It is interesting that China’s production is related to Asian exports, while its exports are related to Western
production and income. This is consistent with the narrative that Chinese manufacturing production involves significant supply chains throughout Asia, while its exports have been heavily dependent on Western
demand.
While New Zealand’s GDP and industrial production are among the Asian country GDP and industrial production variables in the blue sub-cluster, its exports are more similar to the Western – particularly G7 –
country variables in the red sub-cluster.
6 Leader-follower analysis
Although hierarchical clustering analysis shows which countries’ business cycles are most similar, it cannot
describe the source of fluctuations driving them. In order to do so, many studies attempt to identify the
macroeconomic shocks driving these business cycles.
An alternative approach involves a technique called leader-follower analysis, which does not attempt to
identify the source of shocks, but simply considers which countries’ business cycle fluctuations lead to
fluctuations in the business cycles of others. Because this technique cannot identify specific shocks, it
cannot describe the causal mechanisms at work. However, it can describe the flow of business cycle
fluctuations, for example, that fluctuations that affect the Korean business cycle subsequently influence New
Zealand’s business cycle.
A country’s business cycle may be influenced by many other countries’ fluctuations. This is accounted for by
Reserve Bank of New Zealand Analytical Note Series
Figure 4: Dendrogram with Yt , IPt , and Xt 1990Q1:2013Q4
14
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15
using the country-specific business cycles (i.e. adjusted for the effect of the global cycle) and a multivariate
VAR model.14 Thus, the leader-follower relationships identified are independent of the effects of the global
cycle, and account for the endogenous nature of business cycle fluctuations among countries. The following
section describes the VAR model in more detail.
The analysis proceeds in three parts. First, a simple VAR model is specified and estimated. Second,
the business cycle connectedness measures of Diebold and Yılmaz (2013) are employed to discuss the
influence that countries’ business cycles have on other countries. Third, a network diagram is used to
characterize the business cycle flows across countries.
6.1 Vector autoregression model
The VAR model can account for the effects of multiple countries’ influence on a particular business cycle.
Hence, the VAR(p) model contains p lags for each of the N countries in the sample. For a N × 1 vector of
country business cycles, xt , the VAR can be written as:
xt = A1 xt−1 + A2 xt−2 ... + Ap xt−p + et ,
(1)
where xt−l is the l-th lag of the business cycle vector, Al is a N × N matrix of parameters for relationships
at the l-th lag, and et is a N × 1 vector of error terms.
Because there is a large number of countries in the sample and the sample length is relatively short, the
degrees of freedom in the model decline quickly as the lag length increases. Hence, I set p = 2.
The model is run over the full sample, 1990Q1:2013Q4, using the country-specific business cycles (i.e.
adjusted for the effect of the global cycle) as described in section 4.1.
6.2 Business cycle connectedness measures
No identification restrictions are imposed on the VAR model in equation (1). For this reason the error
terms, et , are not orthogonal and thus cannot be interpreted as shocks that independently affect a particular
country’s business cycle. However, the method of Diebold and Yılmaz (2013) avoids the difficulties of shock
identification by employing the generalized forecast error variance decomposition (GFEVD) described in
Pesaran and Shin (1998).
14 Initially
I used bivariate VARs and Granger causality tests for every pair of countries. This seemed a natural econometric extension
of the bivariate lagged correlation method used in the studies of geographic music flows (Lee and Cunningham, 2012) and pigeon
leadership (Nagy et al., 2010). However, the finding that country i leads j ignores the endogenous effect of country k on both i and j.
Reserve Bank of New Zealand Analytical Note Series
16
Rather than exploring the influence of specific shocks, the GFEVD describes the degree of variation in
country j which is accounted for by variations in country i. While the variations in country i may be due to
a variety of underlying shocks, the GFEVD can describe the extent to which movements in i, wherever and
however they originate, are passed on to j.
The GFEVD can be used to describe the influence of different countries in various ways. Diebold and Yılmaz
(2013) define a number of connectedness measures to describe these influences, most of which I reproduce
below.15
• Pairwise directional connectedness: the proportion of country j’s variance explained by country i’s
variance, denoted Cj←i .
• Net pairwise directional connectedness: the net effect of country i on j, given by the difference between the two pairwise directional connectedness countries j and i. Denoted Cji = Ci←j − Cj←i .
• Total directional connectedness to others: the proportion of variance i contributes to all other countries,
given by the sum of the pairwise directional connectedness measures from country i to all countries
j 6= i. Denoted C•←i = ΣN
j=1,j6=i Cj←i
• Total directional connectedness from others: the proportion of variance i explained by all other countries, given by the sum of the pairwise directional connectedness measures to country i from all countries j 6= i. Denoted Ci←• = ΣN
j=1,j6=i Ci←j
• Net total directional connectedness: the net effect of country i on all other countries j 6= i. Denoted
Ci = C•←i − Ci←• .
Table 4 in the appendix reports the GFEVD at the first lag and each of the connectedness calculations
described above.16 The table should be read as showing that the country labeled at the top of column j
explains X percent of the variation in the country labeled in row i one period later.
In this way, the table enables calculations of the net pairwise directional connectedness measure. For
example, fluctuations in New Zealand’s business cycle account for 0 percent of the variation in Australia’s
business cycle, while Australia accounts for 6 percent of the variation in New Zealand. It is interesting to
note that the measure is not especially strong for any country pair, even for those pairs of countries that have
quite similar business cycles as shown in figure 3. However, it may be the case that while countries respond
in a similar fashion to shocks that hit them at the same time (i.e. as shown in the hierarchical clustering
analysis), the spill-over effect of shocks from one country to another is not especially strong (i.e. as shown
in the net pairwise directional connectedness measure).
15 Diebold
and Yılmaz (2013) also define total connectedness as the average effect of all countries’ effects on other countries. However,
this metric is only interesting when measured over time, such as over the rolling window estimation method used in Diebold and Yılmaz
(2013).
16 Note that the full names of countries are reported in the row headers, while the matching two-letter country codes in the column-headers
are those supplied by the International Organization for Standardization.
Reserve Bank of New Zealand Analytical Note Series
17
The column on the far right of the table describes the total directional connectedness from others.17 Numbers
close to 100 indicate that most of the variation in that country’s business cycle (i.e. adjusted for the global
cycle) are accounted for by other countries, while numbers much less than 100 indicate a country accounts
for a lot of its own business cycle variance. Countries that account for a lot of their own variance include
South Africa, the US, Canada, the UK, Spain, Russia, Korea, and China.
It is interesting to note most of the variance in New Zealand’s business cycle fluctuations is explained by the
variance in other countries’ fluctuations at a lag. Thus, while the principal components analysis in table 2
show that New Zealand’s business cycle is not well-explained by the global business cycles within the same
quarter, our business cycle is well-explained by country-specific business cycles one quarter later. This is
reinforced by New Zealand’s role as a ‘follower’ in the network of business cycles discussed below.
The second-to-bottom row describes the total directional connectedness to others. Relatively large numbers
indicate that a country has a lot of influence on other countries, while relatively small numbers indicate that
a country has little influence on others. The table shows that the US, Canada, Italy, Korea, and Malaysia
are influential countries. Korea, to take one example, has had a particularly large influence on Thailand,
Malaysia, Singapore, and Russia. Countries such as Brazil, China, and the Philippines, in contrast, have
very little influence on others over this period.
The bottom row describes the net total directional connectedness. Larger positive numbers indicate countries with a strong leading role among international business cycles, and larger negative numbers indicate
countries that are strong followers. Strong leaders include the US, Canada, Korea, and Malaysia. Strong
followers include New Zealand, Brazil, and the Philippines.
6.3 Leader-follower analysis via network diagrams
The leader-follower analysis presented in this section concentrates on the flow of business cycle movements
between country pairs. Hence, net pairwise directional connectedness is the most useful concept for this
purpose. If Cji = Ci←j − Cj←i > 0, country i has a greater influence on country j than j has on i. That is,
country i leads country j.
These results are neatly presented in a network diagram, which I produced in Gephi, an open source
network graphing software (see Bastian et al., 2009).
The network is interpreted as follows. First, nodes represent countries, and edges (i.e. curves) represent
the direction of business cycle movements between countries. For any two countries, a directed edge exists
from i to j if the net pairwise directional connectedness from i to j is positive, Cji > 0.18 Note that edges
17 Note
that because the errors in the VAR are not orthogonalized, the proportion of the variance of country i explained by all countries
including itself under the GFEVD may exceed 100%. For ease of reading, I normalize the proportions explained so that row sums add
to 100% (see Diebold and Yılmaz, 2013). The far right column is the row sum less the own country contribution.
18 Two-way edges are unnecessary because I am using the net directional concept.
Reserve Bank of New Zealand Analytical Note Series
18
indicating a leading relationship are the same colour as the leading country and also curl in a clockwise
direction from the leading country to the following country. For example, the red, clockwise- curling edges
out of Korea indicate that it leads many countries, including Thailand, Indonesia, and the Philippines.
Second, the weight (i.e. thickness) of an edge from i to j represents the strength of the leader-follower relationship between those countries. The weights are determined by the value for the net pairwise directional
connectedness, Cji . For example, the thick edge from Malaysia to Hong Kong indicates a relatively strong
relationship, while the thin edge from China to Brazil indicates a relatively weak relationship.
Third, the size of the node representing country i is determined by that country’s ‘weighted degree’. This is
defined as the number of leader-follower relationships associated with that country, weighted by the strength
of those relations. Note that the weighted degree metric does not depend on the direction of a country’s relationships. Countries are ranked by their weighted degree, where larger nodes indicate more and/or stronger
leader-follower relationships and smaller nodes indicate fewer and/or weaker leader-follower relationships.
For example, Korea’s large node shows that it is very strongly connected to others, while South Africa’s
small node shows that it is very weakly connected to others.
Fourth, the colour of the node representing country i is determined by its ‘weighted out-degree’. This is
defined as the number of leading relationships that the country has, weighted by the strength of its influence
on other countries. Countries are ranked by their weighted out-degree, where countries that are more red
are stronger leaders, countries that are more blue are stronger followers, and purple countries have a mix
of leader and follower relationships. A legend for this colouring scheme is included in figure the network
diagram. As an example, Korea’s bright red node shows that it is a strong leader, while the Philippines
bright blue node shows that it is a strong follower.
Finally, the layout of the nodes in the diagram is determined by the Force Atlas 2 algorithm provided by
Gephi. This algorithm arranges country nodes so that they are closer to other countries with which they have
the strongest relationships. The algorithm produces networks in which nodes with more and/or stronger relationships form centralized clusters, while nodes with fewer and/or weaker relationships lie on the periphery.
Visually, we can see that larger nodes are more connected to others, and thus tend to be more central to
the network.
Figure 5 is a network diagram showing the results of the pairwise directional connectedness calculations.
As indicated by the size of the nodes, the most connected country is Korea, while Malaysia, Hong Kong,
Thailand, Australia, and Singapore are also very strongly connected to others. This is consistent with Asian
countries being central to global supply chains: business cycle fluctuations are strongly transmitted through
those countries most integrated in global trade patterns.
Reserve Bank of New Zealand Analytical Note Series
19
Figure 5: Network of business cycle relationships, adjusted for the global cycle
The position of the nodes indicates that countries generally cluster into Western and Asian country groups,
as was found in the hierarchical clustering analysis in section 5. That is, not only are business cycle similarities generally clustered by Western and Asian country groups, but leader-follower relationships also fall
into Western and Asian country groups. Again, this is consistent with the global supply chain narrative. If
Asian countries produce a lot of intermediate goods for each other, fluctuations in one Asian country are
likely to pass-through to other Asian countries more strongly than to the countries consuming the eventually
produced final goods.
China, however, is conspicuously absent from the Asian country group. Although this could be an artefact
of the published Chinese data, China’s position in the diagram makes some sense. China is more strongly
connected to Western countries than Asian countries, its connections to Western countries are strong following relationships, and its connections to Asian countries are weaker leading relationships. This suggests
Reserve Bank of New Zealand Analytical Note Series
20
that over this period China’s business cycle tended to follow the cycles of Western countries purchasing its
final goods, while leading the Asian countries that provide the intermediate inputs required to make those
final goods. This is consistent with China’s role as the conduit between Western demand and Asian supply.
As indicated by the colour of the nodes, the strongest leading countries are Korea, Malaysia, the US,
Canada, and Italy. Korea’s leading relationships are mostly directed to countries in the Asian region, although it also has relatively strong influences on New Zealand, Russia, France, and South Africa. Malaysia’s
leading relationships are also mostly in Asia, although it also has a relatively strong influence on Australia.
The United States is not one of the more strongly connected countries, as indicated by its relatively small
node. However, it does have a relatively strong leading influence on the UK, Thailand, Singapore, Australia,
Spain, China, and Canada. However, recall that the leader-follower analysis abstracts from the effect of the
global business cycle. Yet as shown in 2, the fluctuations in US business cycle variables is more strongly
related to the global cycle than those of any other country. It is likely that the influence of the US lies in this
connection to the global cycle. The leader-follower analysis, however, simply shows that the idiosyncratic
component of US business cycles is not as influential as the idiosyncratic component of other countries’
business cycles.
The strongest following countries are China, the Philippines, Indonesia, Brazil, South Africa, and New
Zealand. As noted above, China is likely following fluctuations in demand from Western countries. Indonesia is a mostly a follower of Asian countries, while the Philippines is a relatively strong follower of both
Asian and Western countries. Brazil and South Africa are generally followers of Asian countries.
New Zealand has been a stronger follower of Asian countries than Western countries. It strongly follows
Korea, Singapore, Hong Kong, but also follows Germany, Australia, and Russia.
7 Conclusion
This paper has analysed co-movements and leader-follower relationships between international business
cycles. Key results include that: a significant proportion of the countries’ data variance can be explained by
a global business cycle, similarities between country-specific business cycles tend to cluster into Western
and Asian country groups, and Asian countries’ are the most well connected and among the strongest
business cycle leaders.
Using principal components analysis, I find that a common global component explains around 26 percent of
the same quarter variance in all countries’ business cycles. The global business cycle best explains larger
countries’ business cycles, such as the G7 countries, and is most strongly associated with GDP, exports,
and industrial production. The global cycle itself appears to pick up several important global economic
developments, including the early 1990s recession, the Asian crisis, and the GFC.
Reserve Bank of New Zealand Analytical Note Series
21
Using hierarchical clustering analysis, I find that similarities between country-specific business cycles (i.e.
adjusted for the global business cycle) can be broadly categorized by two large groups: Western and Asian
countries.
Using a VAR model and the connectedness measures of Diebold and Yılmaz (2013), I find that Asian
countries tend to be more connected to and have more influence on other countries than the Western
countries. This is consistent with the narrative that Asian economies have become highly integrated with
global manufacturing supply chains in recent decades. China, in contrast, has been a strong follower of
Western countries and a weaker leader of Asian economies, at least over the period in this study. This
suggests that China has followed cycles in demand that originate in Western countries, and in turn having
some influence on the manufacturing bases in Asia.
New Zealand’s same quarter business cycle fluctuations do not seem to be well-explained by the global
business cycle, at least according to the principal components analysis. In particular, real GDP and nominal
exports are very weakly associated with the global cycle. At least some of this weak association may be
attributed to the influence of local weather patterns on New Zealand’s agricultural production. However,
when the lagged influence of other countries’ business cycles is considered, the model suggests that most
of the variance in New Zealand’s business cycle fluctuations can be explained by country-specific fluctuations. Unsurprisingly, New Zealand tends to follow other countries rather than lead them, although it has
relationships with both Western and Asian economies.
Although these results present a starting point for exploring New Zealand’s connections to the rest of the
world, how best to explain the nature of those connections is likely to require further research.
Reserve Bank of New Zealand Analytical Note Series
22
References
Ahmed, S., B. W. Ickes, P. Wang, and B. S. Yoo (1993). International business cycles. American Economic
Review, 335–359.
Ausloos, M. and R. Lambiotte (2007). Clusters or networks of economies? a macroeconomy study through
gross domestic product. Physica A: Statistical Mechanics and its Applications 382(1), 16–21.
Backus, D. K., P. J. Kehoe, and F. E. Kydland (1992). International real business cycles. Journal of Political
Economy , 745–775.
Bastian, M., S. Heymann, M. Jacomy, et al. (2009). Gephi: an open source software for exploring and
manipulating networks. ICWSM 8, 361–362.
Blanchard, O. J. and D. Quah (1990). The dynamic effects of aggregate demand and supply disturbances.
Technical report, National Bureau of Economic Research.
Buckle, R. A., K. Kim, H. Kirkham, N. McLellan, and J. Sharma (2002). A structural VAR model of the New
Zealand business cycle. New Zealand Treasury, Working Paper , 02/06.
Crucini, M. J., M. A. Kose, and C. Otrok (2011). What are the driving forces of international business cycles?
Review of Economic Dynamics 14(1), 156–175.
Diebold, F. X. and K. Yılmaz (2013). Measuring the dynamics of global business cycle connectedness.
Technical report, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
Gligor, M. and M. Ausloos (2007). Cluster structure of EU-15 countries derived from the correlation matrix
analysis of macroeconomic index fluctuations. The European Physical Journal B 57 (2), 139–146.
Greenwood-Nimmo, M., V. H. Nguyen, and Y. Shin (2012). Probabilistic forecasting of output growth, inflation
and the balance of trade in a GVAR framework. Journal of Applied Econometrics 27 (4), 554–573.
Justiniano, A. and B. Preston (2010). Can structural small open-economy models account for the influence
of foreign disturbances? Journal of International Economics 81(1), 61–74.
Kamber, G., C. McDonald, and G. Price (2013). Drying out: Investigating the economic effects of drought in
New Zealand. Reserve Bank of New Zealand, Analytical Note, 2013/03.
King, R., C. I. Plosser, J. H. Stock, and M. W. Watson (1992). Stochastic trends and economic fluctuations.
Technical report, National Bureau of Economic Research.
Reserve Bank of New Zealand Analytical Note Series
23
Koop, G. and D. Korobilis (2014). Matlab code for bayesian inference in VARs, TVP-VARs and TVP-FAVARs.
http://personal.strath.ac.uk/gary.koop/bayes_matlab_code_by_koop_and_korobilis.html. Accessed: 2014-06-11.
Kose, A., C. Otrok, and C. H. Whiteman (2008). Understanding the evolution of world business cycles.
Journal of International Economics 75(1), 110–130.
Kose, M. A., C. Otrok, and E. Prasad (2012). Global business cycles: convergence or decoupling? International Economic Review 53(2), 511–538.
Kose, M. A., C. Otrok, and C. H. Whiteman (2003). International business cycles: World, region, and
country-specific factors. American Economic Review, 1216–1239.
Kose, M. A., E. S. Prasad, and M. E. Terrones (2003). How does globalization affect the synchronization of
business cycles? American Economic Review, 57–62.
Lee, C. and P. Cunningham (2012). The geographic flow of music. In Proceedings of the 2012 International
Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), pp. 691–695. IEEE
Computer Society.
Miśkiewicz, J. and M. Ausloos (2006). An attempt to observe economy globalization: the cross correlation
distance evolution of the top 19 GDPs. International Journal of Modern Physics C 17 (03), 317–331.
Nagy, M., Z. Ákos, D. Biro, and T. Vicsek (2010). Hierarchical group dynamics in pigeon flocks. Nature 464(7290), 890–893.
Obstfeld, M. and K. Rogoff (2001). The six major puzzles in international macroeconomics: is there a
common cause? In NBER Macroeconomics Annual 2000, Volume 15, pp. 339–412. MIT press.
Pesaran, H. H. and Y. Shin (1998). Generalized impulse response analysis in linear multivariate models.
Economics Letters 58(1), 17–29.
Reddell, M. and C. Sleeman (2008). Some perspectives on past recessions. Reserve Bank of New Zealand,
Bulletin 2.
Redelico, F. O., A. N. Proto, and M. Ausloos (2009). Hierarchical structures in the gross domestic product per capita fluctuation in Latin American countries. Physica A: Statistical Mechanics and its Applications 388(17), 3527–3535.
Sibson, R. (1973). SLINK: an optimally efficient algorithm for the single-link cluster method. The Computer
Journal 16(1), 30–34.
Steenkamp, D. (2014). Structural adjustment in New Zealand since the commodity boom. Reserve Bank of
New Zealand, Analytical Note, 2014/02.
Reserve Bank of New Zealand Analytical Note Series
24
Stock, J. H. and M. W. Watson (2005). Understanding changes in international business cycle dynamics.
Journal of the European Economic Association 3(5), 968–1006.
Treasury (2009). International connections and productivity: Making globalisation work for New Zealand.
New Zealand Treasury, Productivity Paper , 09/01.
Reserve Bank of New Zealand Analytical Note Series
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Appendix
Data Appendix: all series as downloaded from Haver
New Zealand
Australia
South Africa
United States
Canada
Brazil
United Kingdom
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Nominal NZ$, n.s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.
Real private final consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.,
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Civilian employment 16yrs+, s.a.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment 15yrs+, s.a.
Real GDPE, s.a.
Real private final consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment 15yrs+, s.a.
Real GDPE, s.a.
Real household final consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment 16yrs+, s.a.
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1990M1:2013M12
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1990M1:2013M12
2008Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1990M1:2013M12
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1990M1:2013M12
1990M1:2013M12
1996Q1:2013Q4
1996Q1:2013Q4
1996Q1:2013Q4
1990M1:2013M12
1990M1:2013M12
1990M1:2013M12
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1990M1:2013M12
1990Q1:2013Q4
Reserve Bank of New Zealand Analytical Note Series
France
Germany
Italy
Spain
Russia
Japan
Korea
Taiwan
Hong Kong
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.
Real household final consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.
Real household final consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.
Real household final consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment of men 15yrs+, s.a.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, n.s.a.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
26
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1990M1:2013M12
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1991M1:2013M12
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1993M1:2013M12
1990M1:2013M12
1992Q4:2013Q4
1995Q1:2013Q4
1995Q1:2013Q4
1995Q1:2013Q4
1990M1:2013M12
1993M1:2013M12
2002Q1:2013Q4
2003Q1:2013Q4
2003Q1:2013Q4
2003Q1:2013Q4
1991M1:2013M12
1999M1:2013M12
1999Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1990M1:2013M12
1990M1:2013M12
1990Q1:2013Q4
2000Q1:2013Q4
2000Q1:2013Q4
1990M1:2013M12
1990M1:2013M12
1999M6:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1998M1:2013M12
1990M1:2013M12
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1990Q1:2013Q4
1990Q1:2013Q4
Reserve Bank of New Zealand Analytical Note Series
Singapore
China
Malaysia
Indonesia
Thailand
Philippines
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Y
C
I
X
IP
E
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, n.s.a.
Real GDPE, s.a., constructed by Haver
Private consumption, s.a.
Gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
IP excluding construction, s.a., constructed by Haver
Employment in urban units, s.a.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
N/A.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment, s.a.
Real GDPE, s.a.
Real private consumption expenditure, s.a.
Real gross fixed capital formation, s.a.
Exports of goods in US$, s.a.
Manufacturing IP, s.a.
Employment 15yrs+, s.a.
27
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1996M1:2013M12
1990Q4:2013Q4
1990Q1:2013Q4
1990:2013.
1990:2013.
1990M1:2013M12
1990M1:2013M12
1997Q4:2013Q4
1991Q1:2013Q4
2005Q1:2013Q4
1996Q1:2013Q4
1990M1:2013M12
1994M1:2013M12
1998Q1:2013Q4
1990Q1:2013Q4
2000Q1:2013Q4
2000Q1:2013Q4
1990M1:2013M12
1993M1:2013M12
N/A
1993Q1:2013Q4
1993Q1:2013Q4
1993Q1:2013Q4
1991M1:2013M12
2000M1:2013M12
1998Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990Q1:2013Q4
1990M1:2013M12
1998M1:2013M12
1990Q4:2013Q4
Reserve Bank of New Zealand Analytical Note Series
Figure 6: Individual business cycle variable global cycles, 1990Q1:2013Q4
28
Reserve Bank of New Zealand Analytical Note Series
Figure 7: New Zealand data, demeaned and standardised
29
New Zealand
Australia
South Africa
United States
Canada
Brazil
United Kingdom
France
Germany
Italy
Spain
Russia
Japan
Korea
Taiwan
Hong Kong
Singapore
China
Malaysia
Indonesia
Thailand
Philippines
1st
0.163
0.187
0.212
0.236
0.246
0.221
0.238
0.222
0.204
0.230
0.218
0.252
0.224
0.219
0.231
0.211
0.221
0.137
0.233
0.168
0.173
0.202
2nd
-0.016
-0.172
-0.129
-0.216
-0.181
0.127
-0.257
-0.304
-0.107
-0.216
-0.278
-0.139
0.056
0.210
0.136
0.276
0.233
0.265
0.269
0.370
0.240
0.110
3rd
0.554
0.396
0.188
0.087
0.182
-0.130
0.140
-0.196
-0.394
-0.261
-0.136
-0.115
-0.182
-0.008
-0.082
-0.135
-0.078
0.035
0.037
-0.027
0.186
0.170
4th
0.001
0.168
0.166
-0.045
-0.113
0.139
-0.021
-0.104
-0.157
0.100
0.180
0.237
-0.169
-0.069
-0.141
0.078
-0.020
0.655
-0.068
0.028
-0.123
-0.516
5th
-0.002
-0.025
0.523
-0.334
-0.089
0.248
-0.169
0.025
0.209
0.036
-0.001
0.108
0.058
-0.060
-0.409
-0.134
-0.255
-0.164
-0.075
0.244
0.317
0.101
6th
-0.195
-0.033
0.238
0.112
0.223
0.436
-0.023
-0.122
-0.061
-0.251
-0.279
0.103
0.071
-0.382
0.120
0.112
0.068
0.106
-0.204
-0.120
-0.359
0.320
7th
0.157
-0.404
0.119
0.174
-0.015
-0.283
0.204
-0.167
-0.131
-0.153
0.115
0.101
0.527
-0.071
-0.305
0.236
-0.208
0.021
0.175
0.079
-0.199
-0.039
8th
0.236
-0.086
-0.129
-0.087
-0.276
-0.092
0.096
-0.087
0.368
-0.071
0.090
-0.016
0.178
-0.232
0.293
-0.089
-0.227
0.400
-0.260
-0.256
0.290
0.233
9th
-0.289
0.319
-0.275
-0.080
0.047
0.230
0.001
-0.127
-0.275
-0.006
0.087
0.060
0.479
0.313
0.010
-0.354
-0.327
0.072
-0.008
-0.005
-0.022
0.095
Table 3: Principal component coefficients for country business cycles, 1990Q1:2013Q4
10th
-0.393
-0.174
-0.092
0.346
0.263
-0.043
0.086
-0.085
-0.160
-0.126
0.114
0.111
-0.104
-0.330
0.023
-0.114
-0.023
0.009
0.011
0.141
0.603
-0.128
Reserve Bank of New Zealand Analytical Note Series
30
au
6
5
0
0
5
1
1
4
8
10
27
0
0
0
0
0
2
6
0
5
0
1
81
-14
nz
1
0
1
0
10
5
7
0
0
8
0
3
5
1
12
0
1
5
1
0
0
0
60
-39
New Zealand
Australia
South Africa
USA
Canada
Brazil
UK
France
Germany
it
Spain
Russia
Japan
Korea
Taiwan
Hong Kong
Singapore
China
Malaysia
Indonesia
Thailand
Philippines
C•←i
Ci
18
86
za
2
0
30
1
0
3
1
6
0
1
7
10
8
1
12
0
1
0
2
0
0
1
88
157
us
0
13
0
29
12
5
21
5
10
0
9
0
0
3
2
0
14
12
0
1
16
5
63
145
ca
15
7
5
9
18
9
8
5
12
0
3
0
4
0
14
0
5
21
0
0
7
3
-49
44
br
2
0
5
6
1
5
1
3
0
1
1
8
3
0
0
1
0
1
0
2
2
2
-2
79
uk
2
15
2
11
2
1
15
1
5
1
1
3
1
3
0
1
2
0
2
4
7
0
4
97
fr
1
4
0
4
4
9
0
7
1
16
0
1
14
1
1
5
5
2
9
2
4
7
2
97
de
2
1
0
4
7
0
0
13
3
26
1
0
9
1
1
0
3
0
8
0
10
8
51
144
it
4
4
3
4
8
2
0
13
13
6
13
2
15
6
1
5
9
11
11
3
5
6
14
97
es
0
5
4
1
1
5
1
4
0
5
19
7
9
8
0
4
2
4
6
1
0
11
42
116
ru
2
3
10
1
0
1
17
4
11
3
1
24
0
7
13
0
0
6
4
7
2
0
-31
67
jp
2
4
0
0
13
1
0
0
2
1
1
0
2
6
0
2
7
3
5
1
3
14
166
244
kr
16
2
10
8
0
5
2
12
1
6
6
23
9
25
13
10
18
0
21
14
28
15
18
110
tw
8
1
10
4
6
20
13
5
1
0
4
4
1
4
7
5
6
2
0
3
3
3
-8
87
hk
15
4
3
3
4
7
4
1
13
0
3
0
0
6
7
3
5
2
0
4
1
2
Table 4: Connectedness of country business cycles, 1990Q1:2013Q4
-3
96
sg
13
10
7
3
0
13
0
4
3
0
1
6
7
9
3
10
1
1
0
3
2
0
-33
50
cn
0
1
1
3
3
2
2
3
0
1
1
1
0
1
2
1
3
16
0
0
7
2
60
146
my
1
12
2
2
2
0
0
6
3
3
1
3
6
11
9
24
10
1
12
22
2
14
-23
66
id
0
1
3
3
0
4
0
2
1
5
0
3
6
5
1
7
2
0
9
10
1
3
-3
97
th
3
8
2
2
3
0
2
2
11
5
3
0
0
5
0
20
0
6
8
15
0
2
-81
18
ph
5
0
0
0
1
0
1
0
0
1
0
0
1
0
1
0
4
0
0
2
0
2
Ci←•
99
95
68
69
82
93
81
93
95
93
83
74
98
78
92
95
99
83
86
89
100
99
Reserve Bank of New Zealand Analytical Note Series
31