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The Relative Impact of the U.S. and Japanese Business Cycles
on the Australian Economy
By
Hyun-Hoon Lee, Hyeon-seung Huh and David Harris
This paper was presented at the University of Melbourne Economics Department
Workshop and the 1999 Conference of the Korea International Economic Association.
We are grateful to the participants.
Hyun-Hoon Lee: (1) Department of Economics and International Trade, Kangwon
National University, Chunchon, 200-701, Korea. Email: [email protected].
(2) Department of Economics, University of Melbourne, Parkville, Vic 3052, Australia.
Email: [email protected]
Hyeon-seung Huh: Melbourne Institute of Applied Economic and Social Research,
University of Melbourne, Parkville, Vic 3052, Australia.
Email: [email protected]
David Harris: Department of Economics, University of Melbourne, Parkville, Vic 3052,
Australia. Email: [email protected]
Abstract
The purpose of this paper is to assess the relative impact of the U.S. and
Japanese business cycles on the Australian economy. Our vector autoregressive (VAR)
models include real GDPs of three countries and world average oil price, which are
quarterly covering the period 1959:3 – 1996:4. In order to take account of a possible
structural change, estimates are also made separately for the fixed exchange rate and
flexible exchange rate periods. The rolling regression technique is utilised to trace the
patterns and extents of changing importance between the U.S. and Japan’s impacts on
the Australian economy. We find that over the entire sample period, the business cycles
of both the U.S. and Japan have the significant impacts on movements in Australian
GDP. Under the recent flexible exchange rates, however, the impact of U.S. output
becomes greater, while the Japanese impact becomes smaller and negative. It also
appears that U.S. output has significant impacts in both short and longer term, while
Japanese output has little impact in the short term, but greater impact in the longer term.
Table of Contents
1. Introduction
2. General Description of Economic Dependence
Trade and Capital Movement
The Overall Trends of GDPs of Australia, the U.S. and Japan
3. Data and Empirical Specification
Data and Period of Study
Empirical Specification
4. Results
Variance Decomposition
Impulse Response Functions
5. Concluding Remarks
1
1. Introduction
An extensive literature of the modern macroeconomics has documented
international business cycle or the existence of commonalities in economic activity
across countries. Among the issues considered in the field of international business
cycles are sources (demand vs. supply disturbances), transmission under different
exchange regimes (fixed vs. flexible rates) and the channels of transmission
mechanisms (trade vs. financial markets). Identifying the sources and channels of
international business cycle transmission is important for academics and policy makers
alike, because for example, in designing policies to stabilise undesirable disturbances, it
is crucial to know both whether shocks have domestic or foreign origin and whether
transmission occurs through goods or financial markets.
One branch of this literature has addressed these issues in multi-country setting
and has shown that business cycles of different countries are synchronised. Specifically,
several authors, including Cantor and Mark (1988), Backus, Kehoe and Kydland (1992),
Canova and Dellas (1993), and Canova and Marrinan (1998), among others, have
shown that detrended measures of output from different countries are positively
correlated using a variety of methods, and investigated the sources and the transmission
mechanism of business cycles.1
In analysing the synchronisation of international business cycles, a multi-country
framework tends to ignore the relative size of different economies and hence implicitly
assume that any country-specific shocks in a country spill to other countries to the same
extent as those in other countries spill to the country. Therefore this approach may not
1
See Baxter (1995) for a good review.
2
be appropriate in examining the properties of business cycle transmission between small
and large countries.
Accordingly, another branch of international business cycle literature has
concentrated on one small open economy and attempted to show the sources and extents
of foreign influences on this economy. For example, Burbidge and Harrison (1985),
Burdekin and Burkett (1992), and Schmitt-Grohe (1998) investigate the effects of U.S.
economic variables on the Canadian economy. Lee and Lee (1995) assess the relative
impact of U.S. and Japanese economic variables on the Korean economy, which is also
a typical small economy. In their study on the relationship between trade and the
international business cycle synchronisation, Anderson, Kwark and Vahid (1999) take
as a case study the experiences of Korea with its two major trade partners, the U.S. and
Japan. Genberg, Salemi and Swoboda (1987) show that the economic disturbances in
the U.S. and other foreign countries have an impact on the Swish economy. Kim (1999)
shows that the ASEAN business cycles are closely linked to the Japanese cycle and the
link has been substantially strengthened since 1980.
The impact of the foreign business cycles on the Australian economy, which is
also a typical small open economy, has also been documented by a handful of authors.
For Australia, the U.S. and Japan have been the two largest countries in terms of trade
and capital flow, and hence have been the focus of the studies of the foreign business
cycle transmission in Australia. For example, Gruen and Shuetrim (1994) show that the
U.S. business cycle has greater impact on the Australian business cycle than the
business cycles of other trading partners. Similarly, Dungey and Pagan (1996), using a
structural VAR model, find that in the long run the influence of U.S. variables (U.S.
3
GDP, U.S. real interest rates and real share prices) is critically important in determining
domestic activity of Australia.
On the other hand, Magill, Felmingham and Wells (1981) used spectral analysis
and found that Japanese business cycles had a significant influence upon Australian
business cycles between 1958:2 and 1978:1. Selover and Round (1996) highlight
business cycle transmission between Australia and Japan using Vector autoregression
(VAR) and vector error correction (VEC) models. More recently, Summers and Henry
(1999) use a threshold autoregressive (TAR) model to investigate the extent to which
the Australian economy is affected by fluctuations in the economic activity of the U. S.
and Japan. They find evidence that fluctuations in the Japanese economy have a
nonlinear effect on Australia, while there is little evidence that U.S. economic
fluctuations have such effects.
Most of these studies, however, do not compare the relative magnitude of the
transmission from these two countries. They also do not delve into any possible
structural changes of the business cycle transmission owing to the factors such as
Australia’s introduction of floating exchange rates and increased openness to trade and
integration with foreign financial markets. Taking account of any possible structural
changes, this paper attempts to fully assess the relative impact of these two large
countries on Australia. In particular, this paper attempts to answer the following five
questions.
(1) Do the fluctuations in the economic activity of the U.S. and Japan have any
significant influence on the Australian economy?
(2) If so, which country has a stronger influence on the Australian economy?
(3) What has been the trend of such relationships?
4
(4) Does the trend have anything to do with the exchange rate system? In other
words, has the relationship trend have changed since Australia adopted a
crowling peg system in 1977 and a freely floating exchange rate system in
December 1983?
(5) What has been the dominant channel of foreign business cycle transmission?
The goods market through exports or financial market through foreign
capital inflow?
This paper proceeds as follows. Section 2 first describes the trend of
Australia’s economic relationship with the U.S. and Japanese economy in terms of
exports and foreign capital investment, and then depicts the overall trend of the business
cycles of these three countries. Section 3 discusses the univariate properties of the data
and empirical specification of the vector autoregressive (VAR) models. Based on the
models chosen from the discussion in section 3, section 4 reports the results of empirical
tests and estimation. To compare the relationship during each exchange rate regime, the
results are reported separately for the fixed exchanges (1959:3 – 1976:4) and the
floating exchanges (1977:1 – 1996:4), as well as the entire period (1959:3 – 1996:4).
This approach allows us to test the standard theoretical prediction that a given foreign
shock has larger domestic output effects under fixed exchange rates than under flexible
exchange rates. To document thoroughly the overall trend of the results, the rolling
regression technique is also utilised.
Two different empirical results are reported. First, the relative strength of the
causality is also measured with variance decomposition. Secondly, impulse response
functions are also presented for each sub-sample period to show the dynamic nature of
5
the foreign influences on the Australian economy. These results allow us to measure
the extent to which shocks of foreign origin contributed to the observed output
variability in Australia during each exchange rate regime.
2. General Description of Economic Dependence
Two Channels of Business Cycle Transmission
It has been suggested that there are two different channels of transmission of
country-specific shocks across the world. The first transmission channel is through
exports. That is, the foreign business cycle has an impact on the domestic economy by
influencing on its exports (directly through changes in export demand or indirectly
through changes in the terms of trade) and hence the economic activity of the country.
Canova and Dellas (1993) claim that trade interdependencies in intermediate goods are
important in explaining the transmission of country-specific disturbances. More recently
Anderson and Kwark and Vahid (1999) show that the business cycles of countries that
are more open to international trade are more likely to be synchronised with the
business cycles of their major trading partners.
Another channel of international business transmission is the financial market.
Foreign business cycles may have direct impacts on the domestic business cycle
because of the direct influence of foreign asset markets on the domestic asset markets.
Cycle. Pigott (1994) shows that if the foreign country is a large source of foreign capital
inflow to the domestic economy the foreign real interest rates influence the domestic
real interest rates, which in turn determine the domestic business cycle. Canova and De
Nicolo (1995) show that expected U.S. GNP growth helps predict European stock
6
returns which in turn helps to explain future European GNP growth. Froot and Stein
(1991) find that high relative wealth of foreign companies (as a result of an increase on
overseas share prices) induces an increase in foreign direct investment, and hence in the
domestic economic activity.
It can then be inferred that the greater exports to one country and the greater
capital inflow from one country, the higher dependency of the Australian business cycle
on the business cycle of the country.2 Figure 1 illustrates the trends of (a) Australian
exports to and (b) capital inflow from the U.S. and Japan. Japan and the U.S. have been
the first and second largest markets of Australian exports, respectively.3 However the
share in Australia’s total exports attributable to these two countries has been decreasing
since early 1990s. As shown in Figure 1 (a), the share of Australian exports to Japan,
which had peaked in 1976 and had remained above 25 per cent until the early 1990s,
kept declining in recent years and recorded only 19.9 per cent in 1996. The share of
exports to the U. S. was less than half of the Japanese share throughout the entire
sample period except for the beginning year. The U.S. share, which used to be a little
above 10 per cent until the late 1980s, also declined in the 1990s, and recorded only 6.4
per cent in 1996.
Turning to the capital movement, however, the picture is somewhat different.
Until the late 1970s, the amount of foreign capital inflow remained very small. In the
1980s, Australia undertook a gradual liberalisation of its financial market, easing capital
controls (1981), liberalising foreign investment guidelines (1984-87) and deregulating
2
de Roos and Russell (1996) investigate the exports and the share market transmission mechanisms in the
case of Australia. They find that the U.S. and Japan have a high output elasticity of demand for
Australia’s exports. They also find that the U.S. share market has a significant impact on Australian
activity.
3
The U.S. and Japan are also the first and the second largest provider of Australian imports, respectively.
7
the banking system (1985). With this liberalisation of financial market, Australia
became integrated closely with foreign financial markets and the amount of capital
inflow from foreign countries increased dramatically.4 Thus, foreign capital movement
has become closely linked to the Australian domestic economic activity. For example,
total foreign direct investment (FDI) in Australia amounts to an order of 10 to 20
percent of domestic investment. Capital inflow from the U.S. and Japan took a lion’s
share of total foreign capital inflow. Figure 1 (b) shows the trend of the three-quarter
average of net capital inflows (foreign direct investment and portfolio investment) from
these two countries. It is noteworthy that net capital inflow from the U.S. has increased
gradually until recently, while net capital inflow from Japan, which increased until the
late 1980s, has tumbled up and down in 1990s. 5 6
Figures 1 (a) and 1 (b) suggest the general trend of the impacts of the business
cycles of these two countries on the Australian economy. If the export market is the
main channel of business cycle transmission from these two countries to Australia then
the impacts of these two countries on the Australian business cycle should have
decreased. On the other hand, if the financial market is the main transmission channel
then the business cycle impacts of these two countries (especially of the U.S.) should
have increased.
4
Net foreign capital inflow increased from A$1.0 billion in 1979 to A$26 billion in 1989 and to A$34
billion in 1996.
5
Net capital inflow from the U. S. increased from A$0.3 billion in 1979, to A$3.6 billion in 1989, and to
A$ 17.2 billion in 1996. Net capita inflow from Japan increased from A$ 0.2 billion in 1979 to A$8.6
billion in 1989, and declined to A$0.4 billion in 1996.
6
More specifically, the U.S. FDI also represents a significant percentage of the total foreign direct
investment (FDI) in Australia. On the other hand, the Japanese FDI has been relatively small, and has
become negative since 1992. On the other hand, the UK FDI used to be the second largest next to the U.S.
However, the influence of the UK business cycle on the Australian economy has become very small since
1973 when the UK joined the European Community and thus ended Australia’s special economic
relationship with the British commonwealth. In fact, we also included UK GDP in our analysis, and found
that the UK influence is statistically insignificant.
8
The Overall Trends of GDPs of Australia, the U.S. and Japan
Before we move to a formal empirical experiment to investigate how the
Australian business cycle is related with the U.S. and the Japanese business cycles, let
us first consider some graphical representations of the business cycles of these
countries.
Figure 2 shows the plots of the log of real gross domestic products (GDP) of the
U.S., Japan and Australia (GDPus, GDPjp and GDPau, respectively). GDPs of these
three countries are normalised in such a way that their levels at the third quarter of 1959
are set at 100, respectively. The plot of GDP of Japan is relatively smooth compared to
those from the U.S and Australia. Also, it is noteworthy that since the early 1980s the
outputs of the U.S. and Australia move closely together. As seen in Figure 1, this period
coincides with financial market liberalisation and the rapid increase in the capital inflow
from the U.S. However, it is difficult to see if there is much short-run correlation
between Australian output and Japanese output.
Figure 3 shows the linearly detrended plots of the log of real GDPs of (a) the
U.S. and Australia, and (b) Japan and Australia, respectively. It is evident in Figure 3 (a)
that the plots of U.S. and Australian GDPs move very closely together. Again, this comovement has become more evident since the early 1980s. However, Figure 3 (b) does
not seem to show clearly that Japanese and Australian GDPs are related.
Figure 4 shows the plots of the fourth differences of the log of real GDPs (i.e.
annual real growth rate of GDPs) of (a) the U.S. and Australia, and (b) Japan and
Australia, respectively. Similarly to Figure 3, the Australian business cycle seems to be
closely related with the U.S cycle. Again, this close co-movement has been especially
9
evident since the early 1980s. The business cycle link between Japan and Australia does
not seem to be evident.
In the next two sections, we attempt to assess more formally the relative impact
of the U.S. and Japanese business cycles on the Australian economy. The standard
vector autoregressive (VAR) models will be utilised to accomplish this assessment.
3. Data and Empirical Specification
Data and Period of Study
Our VAR models include real GDPs of three countries, which are quarterly
covering the period 1959:3 – 1996:4.7 In addition to the business cycle transmission
channels such as exports and financial markets, certain kinds of common exogenous
shocks to the world may cause countries to cycle together. Specifically, worldwide oil
price shocks may affect directly the economic activity of Australia or indirectly through
the innovations to U.S. and Japanese outputs.8 Thus, world average oil price is also
included in the VAR model in order to account for the effects of such common
exogenous shocks. The data series are all taken from Data Stream. All data are
seasonally adjusted and in the form of natural logs.
When the Bretton Woods system of fixed exchange rates was collapsed and
floating exchange rates were introduced in the early 1970s, many academics and policy
makers alike expected that floating exchange rates would increase the degree to which
7
We also tried real GNP instead of GDP and found little differences. The results with GNP are available
from the authors upon request.
8
In her study on international interdependence of national growth rates, Daniel (1997) shows that oil
price explains a substantial portion of the short-run variation in industrial production for the U.S., the
10
national economies would be insulated from foreign disturbances. However, the high
degree of co-movement of the international business cycles during the post-Bretton
Woods system has led many to question the insulation properties of flexible exchange
rates. On theoretical grounds, many authors argue that flexible exchange rates do not
insulate the national economy from foreign disturbances.9 Some empirical works have
been done comparing the transmission of business cycles across fixed and flexible
exchange rate regimes. For example, Gerlach (1988) finds that output covariances
between the U.S. and most other industrial nations have significantly increased
following the move to floating rates. Baxter and Stockman (1989) argue that the
introduction of flexible exchange rates in the 1970s has not changed the profile of postwar business cycles for most industrial countries. In contrast to these authors, Hutchison
and Walsh (1992) show that the flexible exchange rate regime is more effective in
insulating the Japanese economy from foreign disturbances than is the fixed rate regime.
In Australia, the exchange rate regime started to become more flexible in 1977
with the advent of a crawling peg system.10 In order to investigate whether fluctuations
in the Australian economy are attributable to the change in exchange rate regime,
estimates are also made separately for the fixed exchange rate period (1959:3 – 1976:4)
and the flexible exchange rate period (1977:1 – 1996:4).
U.K. and Japan. Thus with the oil price variable in the VAR model, we can capture the positive oil price
shocks of the 1970s and the negative oil price shock in 1986.
9
See Arthus and Young (1979), Dornbusch (1983), and Glick and Wihlborg (1990), among others. In the
context of different theoretical frameworks, these authors argue that foreign disturbances will affect real
domestic output under flexible rates a much as under fixed rates.
10
Following devaluation of Australian dollar by 12 per cent in November 1976, the Australian exchange
rate system became a crawling peg system - a managed floating system. Australian dollar was freely
floated in December 1983.
11
Empirical Specification
In this paper, ‘business cycle’ refers to the cyclical component of the logarithm
of the seasonally adjusted quarterly gross domestic product. To characterise the cyclical
transmission of output shocks it is necessary to extract the long-run component of the
data. The question arises, however, as to how to best detrend the series.
Until the early 1980s, the common practice of macroeconomists was to assume
that the trend is represented with deterministic functions of time. Since Nelson and
Plosser (1982) macroeconomist have been interested in unit roots in time series.
However, Cochrane (1991) argues that the evidence on unit roots is empirically
ambiguous. Rudebusch (1993) shows that unit-roots tests have low power against
plausible trend-stationary (TS) alternatives. In addition, an extensive literature has
demonstrated that the usual unit-root tests have low power against the null of time trend
stationarity (TS), with structural breaks, and in small samples, among others.11 This has
led Maddala and Kim (1998) to say that “The reason why there are so many unit root
tests is that there is no uniformly powerful test for the unit root hypothesis” (p.47).
A new consensus has been formed that stress the uncertainty about the existence
of a unit root in real output, but no consensus view exists with regard to the appropriate
choice of trend removal.12 Many different detrending methods have been suggested, and
this has also led to considerable controversy as to which trending method is best for any
given purpose.13 As Harding and Pagan (1999) point out that the attention of academics
11
See Maddala and Kim (1998), for an excellent exposition.
For those who are interested in the unit root properties of our data, the results of augmented DickeyFuller (ADF) tests and Phillips and Perron (PP) tests are reported in Table A1 of the Appendix. Most of
the series are better described as an I(1) process with a possible exception of Japan. Both test statistics
indicate that Japan’s GDP is sensitive to the sample periods.
13
Canova (1998) provides a good discussion of the many aspects of the detrending debate. In particular,
he shows stylised facts of U.S. business cycles vary widely across detrending methods and that alternative
detrending filters extract different types of information from the data.
12
12
has increasingly moved towards on cycles in data which have been subject to a rather
complex process of trend removal, whereas the focus of policymakers is largely upon
fluctuations or the ‘classical cycle’ in the level of activity. In this transformation of data,
much information in the series taken to represent economic activity is lost. We believe
that the less transformation of data the more information we get from data. Given the
low power of unit root tests and the detrending debate, we work with the two most
simple and widely used detrending procedures in the macroeconomic literature.
Specifically, we work with a trend-stationary (TS) model and a differencestationary (DS) model. In the TS model, a linear time trend is included along with a
constant and lags of the levels of the log of outputs and oil price.14 In the DS model, a
constant and lags of the first differences of the log of outputs and oil price are included.
Because it is not clear which model is statistically preferable, this double-standing
approach allows us to avoid any possible errors of working with either the TS or DS
model.15
As Canova (1998) notes, this approach also allows us to look at from different
perspectives and examine the sensitivity of our results.16 The TS model assumes that all
variables grow deterministically at the rate of technological change, and thus
innovations to variables have only temporary effects. In the DS model, however,
14
The TS specification is equivalent to working with data that has been detrended via a regression on a
constant and a deterministic trend.
15
If the series is TS process and we use first-differences, then we have overdifferencing, while if it is DS
process and we use levels then we have underdifferencing. There has been some debate in the literature
on the overdifferencing vs. underdifferencing issue. However, McCallum (1993) and Maddala and Kim
(1998), among others argue that if the serial correlation structure is taken into account properly, the issue
of over- vs. underdifferencing becomes nonsense. (See Maddala and Kim, 1998, pp.87-89.)
16
Canova (1998) argues that “different detrending methods are alternative windows which look at series
from different perspectives.” He further argues that “The crucial question is not which method is more
appropriate but whether concepts of cycle are likely to produce alternative information which can be used
to get a better perspective into economic phenomena and to validate theories” (p.477).
13
innovations to variables have permanent effects because any stochastic shock to a
variable contains an element that represents a permanent shift in the level of series.
Lastly, it is to note that we are interested in the cyclical transmission of foreign
variables and hence are less interested in the long-run transmission. Nonetheless, we
applied the Johansen (1988) procedure to test for the evidence of cointegration, because
it is often argued that in the presence of cointegration the VAR analysis with the firstdifferenced variables can lead to the false acceptance of spurious regression
relationships, and in this case, the dynamic relations between the variables should be
represented by an error-correction model (ECM). As shown in Table A2 of the
Appendix, both the trace and maximum eigenvalue tests indicated no cointegration
relationships among the series even at the 10 percent significance level.
4. Results
Variance Decomposition
Table 1 reports the forecast error variance decompositions of Australian GDP at
the forecast horizons of 4, 8 and 32 quarters, generated by the two VAR models. For the
DS model, the estimated VAR models are expanded to models in the levels of the
series, and are inverted to obtain the corresponding variance decompositions.
Accordingly, both the DT and TS models are comparable each other. To draw structural
interpretations, we use a standard Choleski-type of contemporaneous identifying
restrictions. The recursive order of the variables chosen here is OIL, GDPus, GDPjp and
14
GDPau. (This is noted as ‘US-JP’ in the table.)17 Other types of recursive order
produced almost identical results. To save space, we only report in Table 1 the results
when the order of GDPus and GDPjp was switched (‘JP-US’ in the table).
Let us first consider the US-JP column of TS model for the entire sample period
(1959:3-1996:4). The shares accounted for by U.S. GDP (12.87%, 26.16% and 22.44%
at the 4th, 8th and 32nd quarter horizons, respectively) are considerably large. On the
other hand, the shares accounted for by Japanese GDP (1.25%, 3.80%, 14.75% at the
4th, 8th, and 32nd quarter horizons, respectively) are smaller than those by U.S. GDP. It is
to note that the Japanese share becomes larger at the longer-term horizons. That is,
during 1959:3 – 1996:4, U.S. output has significant impacts in both short and long term,
while Japanese output has little impact in the short term, but greater impact in the longer
term. This finding remains robust when we switch the orthogonalisation order of GDPs
of the U.S and Japan, or when we adopt the DS model. Also, unlike the results of
Granger causality tests, oil price has a considerably large impact on Australian output.
The impact of oil price disturbances is especially stronger at the longer-term horizons.
Specifically, oil price contributes to the forecast error variance of Australian output by
3.04, 12.70 and 36.45 per cent at the 4th, 8th and 32nd quarter, respectively. This finding
seems natural, because in variance decompositions worldwide oil price shocks may
affect not only directly the economic activity of Australia but also indirectly through the
innovations to U.S. and Japanese outputs. The proportion of Australian output variance
associated with its own shocks is over 80 per cent at the 4th quarter horizon in both
models, and declines to 26 to 41 per cent (depending upon the models) at the 32nd
17
Thus we treat innovations in world oil price as exogenous shocks to the outputs of Australia and other
countries. We also take the view that the Australian economy is too small to affect the outputs of the U.S.
and Japan.
15
quarter horizon. In other words, the proportion of Australian output variance associated
with foreign shocks is below 20 per cent in the short term, but it is over 50 per cent in
the longer term.
When the estimates are made separately for the fixed exchange rate period
(1959:3-1976:4) and the floating exchange rate period (1977:1-1996:4), some
differences are noticeable. First, during both periods the proportions of Australian
output variance associated with U.S. output are greater than those associated with
Japanese output. Second, during the flexible rate period, U.S. output contributed much
more significantly to the forecast error variance of Australian output than it did during
the fixed rate period at both short- and longer-term horizons. Third, unlike the shares of
U.S. output, those of Japanese output in recent years seem to become smaller at the
short-term horizons (at the 4th and 8th quarter horizons). It is unclear whether the longerterm impact of Japanese output has been changed as in the TS model the Japanese
shares at the 32nd quarter horizon are smaller in the period of flexible rates, yet bigger in
the DS model. Forth, shares accounted for by the oil price, which were the largest
among those by the foreign variables at 8th and 32nd quarter horizons during the fixed
exchange rate period, become very small during the floating exchange rate period. Fifth,
shares accounted for Australian GDP by itself remain about the same in both periods. In
other words, the proportion of Australian output variance associated with foreign shocks
has not changed with the introduction of flexible rates.
In order to ensure the robustness of the results above, the rolling regression
technique is applied to the standard VAR models. The rolling regression technique is
particularly useful here because it allows us to document thoroughly the sub-sample
instability of the results in the VAR models. The forecast error variance decomposition
16
for Australian GDP is first performed using the data from 1959:3 to 1976:4, and then
one additional quarter is added to the data set and the variance decomposition is
repeated. This process is repeated until the entire data set, 1995:3 – 1996:4 is used
Figure 5 shows the plots the shares of the variance of Australian GDP at the 8th
quarter horizon accounted for by the lags of world oil price, U.S. GDP, Japanese GDP
and Australian GDP in (a) the TS model and (b) the DS model, respectively. Only the
results of US-JP ordering are plotted, as the results are insensitive to the changes in the
ordering. The date on which the sample ends is shown on the horizontal axis and the
shares of the variance of Australian GDP are shown on the vertical axis. The recursive
regressions provide us with some new findings. First of all, shares of the variance of
Australian output accounted for by the lags of itself remain amazingly stable throughout
the entire sample period. At the 8th quarter horizon, they were a little over 50 per cent in
the TS model and a little over 60 per cent in the DS model. In other words, the shocks in
the foreign variables (world oil price, U.S. GDP and Japanese GDP) are almost as
important as its own shock in explaining the forecast error variance in domestic output.
On the other hand, the relative shares of the Australian output variance accounted for by
each of the foreign variables have changed noticeably. The relative impact of U.S.
output on the Australian business cycle has increased, while that of Japanese output has
decreased. The shares accounted for by the world oil price has also decreased.
This finding suggests that floating exchange rates in the1970s have not changed
the degree to which the Australian economy has been insulated from foreign
disturbances. It also suggests that the financial liberalisation in the 1970s has been
largely responsible for the changes in the relative magnitude of the impacts of U.S. and
Japanese output. This further suggests that the main channel of the international
17
business cycle transmission to the Australian business cycle has been the financial
market, not trade.
One of the results found in Table 1 was that the impact of the Japanese business
cycle on the Australian business cycle might become bigger in the long run. In order to
investigate the dynamics of business cycle transmission, Figure 6 shows the plots of the
shares accounted by the U.S. GDP and Japanese GDP, at the 8th quarter horizon and
32nd quarter horizon, respectively. In both models, the shares of Japanese output at the
32nd quarter horizon are bigger than those at the 8th quarter horizon. On the other hand,
the shares of U.S. output become a little bit smaller at the 32nd horizon only in the TS
model. Thus we can conclude that the U.S. impact is immediate and remains for quite a
long time, while the Japanese impact is evident only in the long run. This finding also
suggests that the main transmission channel of international business cycle in the case
of Australia is the financial market. That is, the propagation through financial market is
bigger than through Australia’s exports. This further suggests that the main transmission
channel of the U.S. business cycle is the financial market, while that of the Japanese
business cycle is the goods market.
Impulse Response Functions
To investigate explicitly the dynamic properties of international business cycle
transmission, we also calculated the impulse response function results for fixed rates
and floating rates, respectively.
Plotted in Figures 7 and 8 are the impulse responses of the level of Australian
real GDP under fixed and flexible exchange rates to one-standard error shocks in
GDPus, GDPjp, OIL, and GDPau, in the TS and DS models, respectively. The figures
18
largely confirm our earlier findings and in addition clearly indicate the extent to which
disturbances have been dampened or exacerbated during the recent period. It is worth
noting that the short-term responses are roughly similar in both models, but in the
longer term the responses die out in the TS model while they remain stable permanently
in the DS model. This is consistent with the profile of the models, because in the TS
model innovations to variables have only temporary effects, while in the DS model
innovations to variables have permanent effects.
Let us first consider the responses of GDPau to the GDPus and GDPjp shocks in
(a) and (b) of Figures 7 and 7. It is evident that under the flexible exchange regime the
short-term response of GDPau to a GDPus shock is significantly greater, while the
response to a GDPjp shock is significantly smaller. It is also evident that during both
regimes the Australian output response to a Japanese output shock is slower than to a
U.S. output shock. It is worth noting, however, that under flexible rates the Japanese
shock has a negative effect on Australian output. The Australian experience with
Japanese business cycle is at odds with the most literature of international business
cycle which show business cycles of different countries are positively related.18 One
possible interpretation of such negative relationship is that in recent years the slowingdown of Japanese economic activity and the resulting depreciation of the yen relative to
other major currencies including Australian dollar has raised Australian activity by
decreasing the prices of imported inputs.
Figures 7 (c) and 8 (c) indicate that under fixed exchange rates a real oil price
shock causes a considerably large and long-lasting decline in the level of Australian real
GDP. The effect reaches its peak after 8 (DS model) to 12 (TS model) quarters and lasts
19
very long time. Under flexible rates, however, the effect is significantly smaller and
particularly in the TS model it becomes positive in the longer term. This is consistent
with the fact that the oil shock reaches the Australian economy more indirectly than
directly. That is, an oil shock results in a decline in the economic activity of the U.S.,
which in turn causes an immediate decline in the Australian economic activity. The oil
shock also results in a decline in the economic activity of Japan, which in turn causes in
the longer term an increase in the Australian economic activity under flexible rates (as
discussed above). Thus the combined effect of oil shock should be smaller under
flexible rates than under fixed rates, and it could be even negative in the longer-term
because of the indirect effect through the change in the Japanese economic activity.
Finally Figures 7 (d) and 8 (d) show that responses of Australian GDP to its own
domestic shocks have similar pattern of dynamics under fixed and flexible rates. It
seems, however, that the responses are dampened at the longer-term horizons under
flexible rates relative to similar shocks under fixed rates.
5. Concluding Remarks
This study has focused on measuring the magnitude and timing of business cycle
transmission from the U.S. and Japan to Australia, respectively, and attempted to detect
any differences between the transmission under the fixed and flexible exchange rate
regimes. Acknowledging the recent debate on detrending methods, we have worked
with the two most simple and commonly used detrending procedures: the TS and DS
18
Exceptionally, Hutchison and Walsh (1992) report that under flexible exchange rates U.S. output has a
negative impact on Japanese output.
20
models. The qualitative characteristics of the results are largely independent of the
model used.
We have found the following answers to the questions posed in the Introduction.
First, the output fluctuations of the United States and Japan have a large and significant
impact on the Australian business cycle. Along with the world oil price, the foreign
factors are responsible nearly 50 per cent of fluctuations in Australian output at the 8th
quarter horizon.
Second, on the Australian business cycle the U.S. business cycle has a stronger
impact than the Japanese business cycle. This finding is consistent with the common
belief that “when the U.S. sneezes, Australia catches pneumonia,” or “the U.S. economy
is a ‘locomotive’ for the world economy.” This is also consistent with Gruen and
Shuetrim (1994) who find that the U.S. business cycle has greater impact on the
Australian business cycle than other trading partner’s business cycle. This is also
consistent with Dungey and Pagan (1996) who claim that in the long run the influence
of the U.S. variables is critically important in determining domestic activity. Also, this
is in contrast to Summers and Henry (1999) who claim that “fluctuations in Japan have
a nonlinear effect on Australia” and “there is little evidence that U.S. economic
fluctuations have such effects” (p.9).
Third, the link of the business cycles between the U.S. and Australia has been
stronger since early 1980s, while between Japan and Australia it became stronger in the
1980s and then weaker in the 1990s. The second and third findings are in contrast to
Lee and Lee (1995), who find that since 1980s under flexible exchange rates the Korean
economy has a stronger link with Japan than the U.S.
21
Forth, the share of variance of Australian output has remained stable above 50
per cent throughout the entire period. Thus the changes in exchange rate system in
1970s do not seem to have changed the degree to which the Australian economy is
insulated from foreign disturbances. This result does not lead necessarily to the
conclusion that floating exchange rates provide the nation with the same degree of
insulation as fixed rates used to provide. Because this period coincides with significant
trade and financial market liberalisation, it is not clear to what extent the foreign
influence on the Australian business cycle has been attributable to the shift in exchange
rate regime. Given the higher degree of international capital mobility during the period
of flexible, it is more likely that the shift in exchange rate regime in Australia provided
greater insulation from foreign shocks.
Fifth, the relatively stronger impact of the U.S. than Japan (especially since the
early 1980s) on the Australian business cycle suggests that the dominant channel of
foreign business cycle transmission in Australia is not Australia’s exports but the
financial market. The findings that the U.S. impact is immediate while the Japanese
impact is slow also support this argument. This is consistent with Gruen and Shuetrim
(1994) who argue that “because Australia’s business cycle is better explained by US or
OECD activity than by activity in Australia’s trading partners, the transmission
mechanism is not through exports.
Among the findings that are not necessarily answers to the questions posed in
the Introduction is the one with the Japanese business cycle. Inconsistent with
traditional theoretical predictions, a positive disturbance in Japanese output has a
negative effect on Australian output during recent years under flexible exchange rates.
Our interpretation of such negative relationship is that in recent years the slowing-down
22
of Japanese economic activity and the resulting depreciation of the yen relative to
Australian dollar have raised Australian activity by decreasing the prices of imported
inputs.
23
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26
Table 1. Percentages of GDPau Forecast Error Variance Accounted for by the Foreign
Variables
TS
Variables
DS
Horizons
US-JP
JP-US
US-JP
JP-US
1959:3-1996:4
OIL
GDPus
GDPjp
GDPau
4
8
32
4
8
32
4
8
32
4
8
32
3.04
12.70
36.45
12.87
26.16
22.44
1.25
3.80
14.75
82.85
57.34
26.35
12.31
23.50
17.95
1.81
6.46
19.25
2.07
9.45
19.08
13.51
24.03
27.67
0.64
3.86
12.02
83.78
62.66
41.24
13.35
21.75
23.12
0.80
6.14
16.57
6.02
26.15
44.67
8.22
5.60
1.69
0.73
6.51
15.97
85.03
61.75
37.68
8.27
5.33
1.44
0.67
7.13
16.22
1959:3-1976:4
OIL
GDPus
GDPjp
GDPau
4
8
32
4
8
32
4
8
32
4
8
32
8.55
25.54
66.06
17.73
14.55
8.95
4.30
11.51
9.78
69.42
48.40
15.21
17.65
14.16
8.37
4.37
11.90
10.35
1977:1-1996:4
4
1.48
0.40
8
7.82
1.58
32
22.41
2.89
4
34.80
34.75
31.12
31.67
8
44.34
43.82
50.65
52.57
GDPus
32
33.11
31.75
45.80
51.23
4
0.97
1.02
0.91
0.37
8
0.84
1.37
3.78
1.86
GDPjp
32
8.43
9.78
22.05
16.61
4
62.75
67.57
8
47.00
43.99
GDPau
32
36.05
29.26
Notes: Estimated regressions for the ‘TS’ model include a constant, a linear time trend and the four lags
of the levels of the log of real GDPs of three countries and oil price. Estimated regressions for the ‘DS’
model include a constant and the four lags of the first differences of the variables. Numbers are the
fractions of the Australian GDP (GDPau) variance explained by each independent variables after 4, 8 and
32 quarters. The orthogonalisation order of US-JP column is OIL, GDPus, GDPjp, GDPau, and that of JPUS column is OIL, GDPjp, GDPus, GDPau.
Oil price
27
Appendix
Table A1. Unit Root Tests
(a) Augmented Dickey-Fuller Tests
Levels
Variables
No trend
First Differences
Trend
No trend
Trend
-5.13**
-2.92**
-6.13**
-5.95**
-5.26**
-4.84**
-6.48**
-5.98**
-3.80**
-2.43
-4.27**
-3.47**
-4.08**
-3.45**
-4.85**
-3.84**
GDPus
0.34
-2.64
-3.70**
GDPjp
-1.31
-0.93
-2.45
GDPau
0.88
-2.67
-5.15**
OIL
-1.95
-3.25*
-4.80**
Note: ** Significant at the 5 percent level (-2.88 for no trend / -3.43 for trend)
* Significant at the 10 percent level (-2.57 for no trend / -3.13 for trend)
-3.66**
-2.85
-4.43**
-5.27**
1959:3-1996:4
GDPus
GDPjp
GDPau
OIL
-1.60
-3.49**
-2.13
-1.27
-3.25*
-1.74
-2.81
-1.15
1959:3-1976:4
GDPus
GDPjp
GDPau
OIL
-1.69
-2.00
-1.93
0.27
-1.95
-0.14
-1.06
-1.19
1977:1-1996:4
(b) Phillips and Perron Tests
Levels
Variables
No trend
First Differences
Trend
No trend
Trend
-8.99**
-8.59**
-14.55**
-11.75**
-9.49**
-11.05**
-14.30**
-11.71**
-6.00**
-5.94**
-11.17**
-9.00**
-6.75**
-6.91**
-11.04**
-9.23**
GDPus
-0.17
-2.13
-6.62**
GDPjp
-1.92
-0.62
-8.74**
GDPau
-0.10
-2.55
-7.84**
OIL
-2.42
-3.16+
-7.04**
Note: ** Significant at the 5 percent level (-2.88 for no trend / -3.43 for trend)
* Significant at the 10 percent level (-2.57 for no trend / -3.13 for trend)
-7.10**
-9.01**
-7.79**
-7.00**
1959:3-1996:4
GDPus
GDPjp
GDPau
OIL
-1.78
-6.52**
-1.14
-1.28
-2.50
-2.07
-1.81
-1.26
1959:3-1976:4
GDPus
GDPjp
GDPau
OIL
-1.61
-2.85*
-0.48
0.21
-1.33
0.31
-2.64
-1.37
1977:1-1996:4
28
Table A2. Johansen Cointegration Tests
-max tests
59:3-96:4
59:3-76:4
77:1-96:4
59:3-96:4
59:3-76:4
77:1-96:4
R=0
43.33
37.71
35.50
24.65
17.46
21.73
18.68
20.23
13.77
11.89
14.13
11.15
R1
6.79
6.10
2.62
6.68
3.56
2.60
R2
0.11
2.54
0.02
0.11
2.54
0.02
R3
Note: * Significant at the 10 percent level. Critical values for the trace and -max test statistics are drawn
from Osterwald-Lenum (1992).
Trace tests
29