structural var
TRANSCRIPT
Centre for Financial Analysis & Policy
Working Paper no. 41:
Can External Shocks Explain the Asian Side of Global Imbalances? Lessons from a Structural VAR Model with Block Exogeneity Jean-Baptiste GOSSE & Cyriac GUILLAUMIN March 2012
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The Working Paper is intended as a mean whereby researchers’ thoughts and findings may be communicated to interested readers for their comments. The paper should be considered preliminary in nature and may require substantial revision. Accordingly, a Working Paper should not be quoted nor the data referred to without the written consent of the author(s). All rights reserved.
© 2012 Jean-Baptiste Gosse & Cyriac Guillaumin
Comments and suggestions would be welcomed by the author(s).
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Can External Shocks Explain the Asian Side of Global Imbalances? Lessons from a Structural VAR Model with Block Exogeneity
Jean-Baptiste Gossé1 Cyriac Guillaumin**
ABSTRACT During the last decade, we observed the accumulation of global imbalances resulting primarily from massive current account imbalances in the US and in Asia. Most of the attention has been focused on the US side of global imbalances and few studies have been dedicated to the large current account surpluses in Asia. The aim of this paper is to study the impact of external shocks on Asian countries in order to determine if these can account for the Asian side of global imbalances. To this end, we estimate a structural VAR model with block exogeneity using contemporaneous and long-run restrictions and Bayesian inference. The three external shocks of our model are an oil shock, a US monetary shock and a US financial shock. Our main findings are as follows: (i) external shocks account for the current account surplus in Korea, Malaysia, the Philippines, Singapore and Thailand and, to a lesser extent, in Japan and Indonesia; (ii) the oil shock and the US monetary shock seem to have influenced current account balances through real and monetary channels, and the US financial shock through financial channel. Keywords: Current Account Imbalances, External Shocks, Structural VAR Models, Block Exogeneity, East Asia. JEL Classification: F32, F41, G15.
* CFAP, University of Cambridge, Trumpington Street, Cambridge CB2 1AG, United Kingdom. E-mail: [email protected]. ** CREG, University of Grenoble, 1241 rue des Résidences, Domaine Universitaire, BP 47, 38040 Grenoble Cedex 9, France. E-mail: [email protected]. Corresponding author: Jean-Baptiste Gossé, CFAP, Cambridge Judge Business School, Trumpington Street, Cambridge CB2 1AG, United Kingdom. E-mail: [email protected]. Phone: +44 (0) 1223 765341. Fax: +44 (0) 1223 339701.
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1. Introduction
We have seen an unprecedented accumulation of global imbalances during the last decade
(Blanchard and Milesi-Ferretti, 2010). From 2000 to 2010 net inflows of savings to the United
States, much of which came from Asia, were 6200 billions of dollars (Eichengreen, 20062). At
the same time, we observed three major shocks to the global economy which may have played
a role in the formation of global imbalances: an increase in the oil price, very low interest
rates and two financial crises in the US. Much of the attention has focused on the historically
large US current account deficit (Obstfeld and Rogoff, 2004) and only a few articles have
studied the surplus countries.3 This article examines the impact of external shocks on nine
Asian economies in order to determine whether they can explain the current account surpluses
observed in these countries during the last decade.
On the deficit side, the United States has been running a growing current account deficit and
has been the major recipient of global net saving. In the 2000s, the US deficit was, on
average, above 4.5% of GDP. A first episode of global imbalances was already observed in
the 1980s. In this period the United States was the source of deficits and Japan the main origin
of surpluses. These imbalances were drastically reduced in the early 1990s. However, after
the Asian crisis of 1997-98, they began to grow again. In this new episode of global
imbalances, Japan is not alone on the surplus side (Figure B.1). Emerging Asia and, to a lesser
extent, OPEC countries, became important sources of net saving (Caballero and
Krishnamurthy, 2009; Blanchard and Milesi-Ferretti, 2011). The Japanese current account
balance surpluses of the 2000s were, on average, close to 3.5% of GDP, Korea became a
surplus country with an average current account surplus of 2.3% and Chinese surplus grew
from 0.5% of GDP in 2000 to 2.3% in 2010 (Figures B.2 and B.3).
There is an extensive literature on the recent accumulation of global imbalances (see, for
example, Aslund and Dabrowski, 2008 and Angeloni et al., 2011, for a survey). A first
2 See also Huang and Wang (2010) for a special study of China. 3 Gossé and Guillaumin (2010) study the case of the euro zone countries but only some of them have a current account surplus.
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approach considers that the origin of imbalances is on the surplus side. Dooley et al. (2003,
2004) argue for a new Bretton Woods regime in which Asian emerging countries need to run
trade surpluses to accumulate US assets that are used as collateral for FDI. Before his
nomination as chairman of the Federal Reserve, Bernanke (2005)4 claims that global
imbalances result from excessive saving – which is often linked to a decline in the rate of
capital investment in Asia – in Asian and OPEC countries. The saving glut then went to the
US because of its attractiveness and the specific status of the dollar (Gourinchas and Rey,
2005; Cooper, 2008, Eichengreen, 2011).
A second approach considers that the source of imbalances is on the deficit side; that is to say
in the United States (White, 2006). The monetary approach suggests that US deficit results
from the US monetary policy that stimulated domestic demand and led to a rise in imports
combined with a deterioration in the terms of trade (Bems et al., 2007; Rajan, 2010). Thus, the
reduction of interest rates following the collapse of the dot-com bubble could explain the
rapid growth in the US current account deficit. Furthermore, the increase in asset prices may
also explain a higher current account deficit since it makes consumers feel wealthier and they
are more inclined to reduce their savings (Gros et al., 2006).
Many empirical papers study the causes and adjustment mechanisms of current account
imbalances using a VAR model. Lee and Chinn (1998, 2006) estimate simple bivariate VAR
models including exchange rates and current account balance. They use long-run restrictions
to identify the structural shocks, as in Blanchard and Quah (1989). The results suggest that
temporary and, to a lesser extent, permanent shocks improve the current account balance of
most G7 countries. Cashin and McDermott (2002) and Giuliodori (2004) add a demand shock
to Lee and Chinn’s model by including the GDP in their models. The results of these two
papers confirm previous findings and point out that a supply shock has a negative impact on
the current account balance in OECD countries. We build on these empirical studies in order
to define the domestic block of our model. The external block comprises the three external
shocks, the impact of which we want to study (the growth differential, the real effective
exchange rate and the current account balance). We follow the methodology developed by
Maćkowiak (2007) and Sato et al. (2011) to identify our external shocks.
This paper has two aims. The first is to identify the adjustment channels of external shocks in
Asian economies. The second is to determine the extent to which external shocks account for
the Asian side of global imbalances. We use a Bayesian VAR (BVAR) model with block
4 See also Chinn and Ito (2007) and Caballero et al. (2008a, 2008b).
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exogeneity to address these issues since this approach has been found to improve upon the
forecasting performance of other empirical models.
The results of our estimates indicate that external shocks account for the current account
surplus in Korea, Malaysia, the Philippines, Singapore and Thailand and, to a lesser extent, in
Japan and Indonesia, but they do not explain the evolution of the current account balances of
China and Hong Kong. The oil shock explains a big part of the long run variance of domestic
variables in all countries except China, Hong Kong and Indonesia. The US monetary shock
accounts for current account balance variations in Indonesia and Korea. The US financial
shock is a major source of current account balance fluctuations in Indonesia, Malaysia and
Thailand.
The remainder of the paper is organized as follows. Section 2 presents the methodological
framework used in this paper. Section 3 discusses our main empirical results. Section 4 offers
conclusions.
2. The Model
2.1. SVAR model with block exogeneity
In the context of strong links of macroeconomic variables with complex feed-back linkages,
VAR models constitute useful tools to catch the interdependencies between multiple time
series. In order to analyze the transmission channels of external shocks on Asian countries,
consider the following structural VAR (SVAR) model with block exogeneity:
t
t
sty
sty
sAsA
sAsAp
s 2
1
2
1
0 2221
1211
(1)
where 012 sA for each ps ,...,1,0 . sty 1 is a vector of external variables and sty 2
is a vector of domestic variables. t1 is a vector of structural shocks of external origin and
t2 is a vector of structural shocks of domestic origin. ', 21 ttt is a Gaussian
random vector satisfying 00, sstytE and IsstyttE 0,' with I
the identity matrix.
The vector of external variables sty 1 includes the following variables: the real oil price
(rBrent), the Fed Funds interest rate (Fed Funds) and the S&P 500 index (SP500). The vector
of domestic variables sty 2 includes the real growth differential (y/y*), the real effective
exchange rate (reer) and the current account balance (ca/y).
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The model is formulated separately for each country and assumes that Asian countries are
small enough not to affect world variables. This assumption implies the block exogeneity
restriction 012 sA for each ps ,...,1,0 which indicates that domestic shocks t2 do not
affect the external variables in the vector sty 1 either contemporaneously or with lags.
Introducing the exchange rate is justified by the nature of this variable which, whatever the
source of the shock, constitutes an important transmission mechanism, as Cushman and Zha
(1997) show. In addition, unlike some studies privileging a nominal exchange rate and from it
obtaining stronger reactions, we prefer to use the real effective exchange rate in order to
capture all the real effects conveyed by external trade as in Canova (2005).5 The introduction
of an external shock, on the other hand, allows for a better account of the influence of external
factors upon the economic dynamics of these economies. The decomposition of this external
shock into three shock types (real, monetary and financial) is performed in order better to take
into account the nature of the hazard to which Asian countries may be exposed. Firstly, most
of these economies import raw materials for their industries and depend heavily on the
exterior (Cushman and Zha, 1999). Moreover, they have adopted an exchange rate pegged to
the US dollar or to a currency basket where the dollar represents between 80% and 95%
(Reinhart and Rogoff, 2004; Ilzetzki et al., 2009). Finally, they benefited and keep benefiting
from capital inflows. And yet these flows can become highly volatile in the event of an
external financial shock (Calvo et al., 1994). However, the generous parameterization of most
VAR models can lead to a deterioration in forecasting performance. Employing parsimonious
model specifications is one way to address this issue. However, that also means that
interpretability is sacrificed to a greater or lesser extent, as the number of questions that can
be addressed becomes limited. An alternative approach is to rely on Bayesian VAR modeling,
which reduces the degrees of freedom problem by introducing relevant prior information.
2.2. Restrictions and BVAR methodology
The identification of the structural form requires imposing 2/1nn restrictions. We thus
need fifteen of them. Our model uses short-run and long-run restrictions and the exogeneity
hypothesis. Following Zha (1999), Cushman and Zha (1997), Elbourne and de Haan (2006),
5 Canova (2005) uses nominal and real effective exchange rates. For example, Maćkowiak (2007) or Gimet (2011) privilege a nominal bilateral exchange rate on U.S. dollar for Mercosur countries justified by intense commercial and financial relations as well as by an exchange regime more or less pegged to the US dollar. Besides, the authors do not take into account the geography and composition of these economies’ external trade. Yet we wish to consider these last two elements in our study.
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Maćkowiak (2007), Gossé and Guillaumin (2010) and Sato et al. (2011), we impose the
following constraints.
The block exogeneity restriction implies that domestic structural shocks t2 do not affect
the vector of external variables sty 1 at time t or st . We thus impose nine constraints.
Identification of US monetary policy follows Leeper et al. (1996). It is assumed that the Fed
Funds interest rate can respond contemporaneously to changes in the real oil price.
Concerning the external block, it is assumed that real oil prices are not affected by the two
others external shocks regardless of the number of delays. We obtain two additional
constraints. Furthermore, we assume that the U.S. interest rate is not affected by short-term
stock market volatility (Sato et al., 2011). Thus, we get one additional constraint. Regarding
the domestic block, we impose three long-run zero restrictions, as in Blanchard and Quah
(1989), Clarida and Galì (1994) and Sims and Zha (1999), which are: (i) a domestic demand
shock has no impact on the domestic product and (ii) a monetary domestic shock has no
impact on the domestic product and on the real effective exchange rate.
The data used are quarterly and cover the period 1981:1-2010:36 in order to include the main
economic episodes which have marked the integration dynamics of Asian countries (1997
crisis, 2007-2008 crisis, setting up of financial and monetary regional agreements7). Every
variable except U.S. interest rate (Fed Funds) and the current account has been turned into
logarithms. GDP and current account data have been deseasonalized.8 We first test our series’
order of integration for each variable and each country through unit root tests.9 Most variables
are stationary in first difference. Cointegration tests reveal long-term relationship between
variables for each country.10 We thus have the choice between estimating a VAR model in
level or a VECM. We can choose to estimate a VAR model in level following Sims et al.
(1990) and Sims (1992). These works show that the common practice of attempting to
transform models to stationary form by difference or cointegration operators, whenever it
appears likely that the data are integrated, is in many cases unnecessary. We then estimate the
equations in levels.
6 Data are described in Appendix A. 7 See for instance Henning (2002), Yip (2008) or Guillaumin (2009) for the details of these agreements. 8 Census X-12 method. Current account data have been deseasonalized using Census X-11 method because multiplicative and log-additive adjustments do not allow negative data. 9 Tests with and without breaks were conducted. Those without breaks are ADF and Phillips-Perron (PP) tests. For those with breaks, we used Perron (1989) in which break date is exogenous. In that case, we choose 1997.2 as a break date: it is indeed after the second quarter of 1997 that the crisis develops in earnest (Rüffer et al., 2007). We used Zivot and Andrews (1992) and Clemente et al. (1998) method to test unit root in which break date is endogenous. Details of these unit root tests are available upon request from the authors. 10 Details of these cointegration tests are available upon request from the authors.
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A Bayesian approach finds no reason ever to use a transformed model, except possibly for
computational simplicity. That is why we decide to estimate a Bayesian VAR (BVAR) model
for each country. As in Kim and Roubini (2008), our statistical inference is not affected by the
presence of unit roots and cointegrating relations, since we follow a Bayesian inference to
construct standard errors of impulse response functions and to forecast error variance
decomposition. We use one year plus two periods of lags (Doan, 2010).11 The out-of-sample
forecasting performances of the BVAR model with block exogeneity is compared with that of
a simple BVAR and to a naïve forecast using OLS method.
The root mean square error (RMSE) enables us to compare the forecasting performances eight
quarters ahead of the three models on the period 1997Q1-2010Q3. The VAR model is first
estimated on the period 1981Q1-1996Q4 and we then use the Kalman filter to update the
coefficients. A relative RMSE smaller than one indicates that the mean-adjusted BVAR
forecasts better than the alternative model at a given forecasting horizon. Results are
presented in Table 1. We can see that the BVAR model with block exogeneity almost always
outperforms the other models (*). The only exception is Singapore where the relative
difference is weak.
OLS Simple BVAR BVAR with block exogeneity
China 1 0.9563 0.9470*
Hong Kong 1 0.9731 0.9404* Indonesia 1 0.8574 0.8217* Japan 1 0.9619 0.9129* Korea 1 0.6721 0.6483* Malaysia 1 0.9123 0.9070* Philippines 1 0.9512 0.9481* Singapore 1 0.9636* 0.9671 Thailand 1 0.9716 0.9376*
Table 1 : Forecasting performance of alternative models (Relative Root Mean Square Errors)
Finally, we introduce a dummy variable which equals one from 1997:2 to 1998:3 and zero
otherwise (Asian crisis), following Rüffer et al. (2007).12 We also introduce a dummy variable
which equals one from 2008:3 to 2009:2 and 0 otherwise, in order to take into account the
11 The standard errors are constructed by Monte Carlo integration with the Jeffrey's prior as in Doan (2010) and Kim and Roubini (2008). 12 We introduced alternatively a dummy for the post-Asian crisis period but this variable was not significant.
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effects of the world crisis (started with the subprime crisis), following Berthaud and Colliac
(2010).
3. Results and discussion
3.1. Analysis of the contribution of external shocks
We perform a variance decomposition analysis in order to determine the sources of
fluctuations of each domestic variable ( */ yy ; eerr ; yca / ). We focus here only on the impact
of external shocks on the variations of the three domestic variables. The results of the
variance decomposition analysis for the nine Asian countries are summed up in Table C.1 for
the short and longer term.
We can first make some general observations. We note that the GDP differential (except in
the cases of Indonesia, Korea and Thailand), the real exchange rate (except Singapore) and
the current account balance (except China and Hong Kong) are strongly affected by external
shocks. In all the cases, these shocks explain more than a fifth of the variance. China and
Hong Kong are two very interesting cases. In these countries, the external shocks contribute
hugely to the variances of the GDP differential and the real effective exchange rate, but they
have a very small impact on the current account balance.
The oil shock is the main source of fluctuations of the current account balance. The impact of
this shock is particularly important in Singapore, Malaysia and the Philippines, where it
explains, respectively, 26%, 38% and 43% of the variations. The US monetary shock has the
most powerful impact on current account balance in Indonesia, Korea and Singapore. The
strongest effect of the US financial shock on the current account balance is felt in Thailand
and Indonesia and, to a lesser extent, Korea and Malaysia. It is also worth noting that the
increase in S&P 500 index affects quite significantly the short-run net capital movements in
Japan, the country with the highest capital mobility.
3.2. The effects of external shocks on Asia
The study of impulse response function enables us to observe the reactions of current account
balance following an external shock and to observe the adjustment channels at the domestic
level.13 It is important to distinguish short-term effects, which result from the direct influence
of the external shocks on the current account balance, and medium-term, indirect effects,
13 The solid line is the median of the posterior distribution and the dashed lines represent the 16th and 84th quantiles which correspond to a one standard deviation band.
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which are often due to changes in the real effective exchange rate and the GDP differential
resulting from external shocks.
The oil shock produces three different types of reaction (Figure D.1). The first group is
composed of the oil producing countries (Table B.1). Indonesia endures a kind of Dutch
disease effect. In the short-term, the oil price effect dominates and leads to a surplus, but in
the medium-term the appreciation of the real effective exchange rate (REER) deteriorates the
current account balance. In China, this shock has a positive impact on the growth differential,
which means that its GDP is significantly less negatively affected by oil shocks than in the
rest of the world. However, the Chinese current account balance is not significantly affected.
Malaysia does not suffer from any Dutch disease because its REER is not affected by the
increase in the oil price. In consequence, the Malaysian current account improves persistently,
since the elasticity of substitution of oil products is small. Singapore is a very special case, as
the country is not an oil producer but is one of the main refining centers in the world, which
explains the large oil products exports in this country (see Table B.1). This may explain why
the oil shock triggers an improvement of Singaporean current account balance in the same
way as in Malaysia.
In the second group, that of the non-oil producing countries, the oil shock provokes a current
account surplus, except in Hong Kong. In Japan and Philippines, we observe a J-curve
reaction of the current account balance as in Gupta-Kapoor and Ramakrishnan (1999). In the
short-term, the price effect is stronger and the current account deteriorates. In the medium-
term, the volume effect becomes stronger and the current account improves. We also observe
a real depreciation in Hong Kong, but it has no significant effect on the current account
balance. In the third group composed of Korea and Thailand, the negative impact on the
growth differential allows adjusting the small deterioration of the current account balance in
the short-term, producing a surplus in the longer term.
The increase in the Federal funds rate appreciates the REER of all the countries but Japan –
the only country with a flexible exchange rate (see Table B.1) – where it depreciates (Figure
D.2). The degree of exchange rate flexibility is very low in the other countries, which can
explain why their currencies match, at least partially, the appreciation of the dollar resulting
from higher interest rates in the US. At the same time, for all but two the current account
balance deteriorates, which is consistent with the real appreciation.
Surprisingly, in China and Hong Kong – where the currencies are closely pegged to the US
dollar – the increase in the exchange rate is accompanied by a current account surplus. In fact,
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in that case the negative growth differential effect seems to dominate the appreciation effect.
As a result, the current account balance improves in these two countries. We can also note
that the GDP differential is negatively affected by the monetary shock, except in Indonesia
where its improvement reinforces the deterioration of the current account balance, and in
Japan, where it is not significantly affected. It is worth noting that these two countries are
among the less dependent on the US (see Table B.1).
The case of Japan is interesting. The increase in the US interest rate provokes a yen
depreciation, as demonstrated in Jang and Ogaki (2001) and Bluedorn and Bowdler (2011),
and should produce a current account surplus. However, we observe a deterioration in the
current account balance. This suggests that Japan may play a very particular role in the
international monetary system: when the US interest rate becomes higher, international
investors borrow yen at low interest rates – Japan initiated a policy of near-zero interest rate
in the 1990s – and then switch those borrowings into dollars and other currencies in order to
benefit from lower interest rates.14
The reactions to the US financial shock may be divided into three categories (Figure D.3).
Firstly, China and the Philippines are two countries presenting very high levels of capital
control (see Table B.1) where, consequently, the financial shock does not produce capital
outflows and even results in a small current account deficit following the increase in their
GDP differential. The second group is composed of the most financially integrated countries
that follow a fixed exchange rate policy. The averages of the financial integration indexes
(Table B.1) are, respectively, 1111%, 506% and 158% for Hong Kong, Singapore and
Malaysia.15 In this case, the impact of the increase in the S&P 500 index is essentially
financial. Investors of these countries are attracted by higher returns in the US which lead to
net capital outflows reflected in a current account surplus. At the same time, the increase in
the price of foreign assets generates a wealth effect which has a positive impact on their GDP.
The third group – Japan, Korea, Indonesia and Thailand – has very heterogeneous features but
experiences highly homogeneous reactions to the US financial shock. In this case, the shock
produces net capital outflows for a long period, leading to a persistent current account surplus.
This lasting net outflow of capital has a negative impact on the nominal exchange rate,
triggering a depreciation of the real effective exchange rate. It is worth noting that the
14 For a complete discussion of these so called “carry trade” strategies, see Gagnon and Chaboud (2007), Lustig and Verdelhan (2009) or Hutchinson and Susko (2010). 15 The index presented here is an average for 1981-2004. For more details, see Lane and Milesi-Ferretti (2007).
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exchange rates and current account balance responses are negatively correlated. Indeed, the
increase in the S&P 500 index leads to an increase in the demand of assets in dollars which
implies both capital outflows and a decrease in the demand for the domestic currency relative
to foreign currency. Finally, the decline in the Japanese, Indonesian and Thai growth
differentials may be explained by the higher growth rates resulting from the US financial
shock in the rest of the world – in particular in the United States – than in Japan, Indonesia
and Thailand.16
For China, Hong Kong and Malaysia, as we have obtained only annual data for the current
account, we initially take the quarterly trade balance data for our model. However, we choose
to interpolate each annual current account series for these countries in order to obtain
quarterly data. We use the proportional Denton method. This method is robust (Chen, 2007)
and recommended in IMF and Federal Reserve Bank publications (see, for example, Kinda,
2011; Liu et al., 2011).17 To test robustness, we then compared models using trade balance
data and interpolated current account data. For China, Malaysia and Singapore, results are
remarkably similar. External shocks, oil shock for China, monetary shock for Malaysia and
financial shock for Singapore, are more significant with interpolated current account data than
with trade balance data. Results for Hong Kong are quite different but mostly insignificant
except for the oil shock which provokes a current account surplus instead of a deficit when we
use trade balance data.
To the best of our knowledge, there are very few papers dealing with the impact of external
shocks on Asian economies and none of them studies their effect on the current account
balance. Sato et al. (2011) use a SVAR model with block exogeneity to investigate whether
external shocks affect macroeconomic fluctuations in Asian countries. Although their model
does not include the exchange rate and the current account balance, their results show a
dynamic effect of external shocks which confirms the influence of the US shock and of the oil
shock on Asian economies. Maćkowiak (2007) estimates a SVAR model which shows that
the US monetary policy shock has a positive impact on the real effective exchange rate of
Korea, the Philippines and Thailand, and an ambiguous effect on the other Asian countries. At
the same time, he shows that the effect of the US monetary policy shocks on Asian markets
16 The impact of the US financial shock is not significant for the Indonesian GDP differential. 17 To test the robustness of the interpolation method, we made the same calculations for countries on which we have quarterly current account data. We obtained a strong correlation coefficient between the effective series and the calculated quarterly series. Results are available upon request from the authors.
14
does not seem as important as the effect of the other external shocks such as world
commodity prices, US output or US price level.
4. Conclusion
This article investigated the impact of external shocks on the Asian current account balances.
Firstly, we have put in evidence the channels of transmission by which external shocks affect
Asian current account balance. The oil and the US monetary shocks play a huge role in
explaining Asian current account balances. However, the mechanisms explaining these
responses differ. The oil shock triggers either a depreciation, which leads to a current account
surplus in the medium-term, or a slowdown in relative growth, which has the same effect. The
US monetary shock is mostly adjusted through the exchange rate and the GDP differential.
Since all the countries of the sample – except Japan which experienced a depreciation – are
quite strongly pegged to the US dollar, the exchange rate appreciates, which leads to a current
account deficit. The corollary of this is that a decline in the US interest rate would lead to a
depreciation and imply current account surpluses. In some countries, the monetary shock
cannot explain the current account surplus, since the GDP differential effect dominates the
exchange rate effect (China and Hong Kong). The financial shock mostly contributes to
explain the Indonesian and Thai surpluses. It is notable that the two most affected countries
share common characteristics: they do not impose a tight capital control, they are not strongly
financially integrated, and they have a very low level of financial development. Secondly, the
results of our estimates indicate that these shocks explain very well the current account
surplus in Korea, Malaysia, the Philippines, Singapore and Thailand and, to a lesser extent, in
Japan and Indonesia. Indeed, half of external shocks help to explain a significant part of the
current account balance in the medium-term (see Table D.1). However, these shocks have
failed to explain the current account surpluses of China and Hong Kong, with results
indicating either the opposite effect or an insignificant impact.
Acknowledgements
We are grateful to T. Doan, L. Hanzlik, A. Lopez-Villavicencio and L. Vanessa Smith for
highly profitable discussions and comments. We also thank the participants of the Cambridge
Finance seminar. All remaining errors are ours.
15
References
Angeloni, I., Bénassy-Quéré, A., Carton, B., Darvas, Z., Destais, C., Pisani-Ferry, J., Sapir, J.
and Vallée, S., 2011. Global currencies for tomorrow: a European perspective, Blueprint
Series, 13, Bruegel.
Aslund, A. and Dabrowski, M., 2008. Challenges of Globalization: Imbalances and Growth.
The Peterson Institute for International Economics.
Bems, R., Dedola, L. and Smets, F., 2007. US imbalances: The role of technology and policy.
Journal of International Money and Finance 26, 523-545.
Berthaud, F. and Colliac, S., 2010. Which emerging countries have experienced a sudden stop
of capital inflows during the recent crisis? Trésor-Economics 76.
Bernanke, B., 2005. The global saving glut and the U.S. current account deficit. Sandridge
Lecture, Virginia Association of Economics, Richmond, Virginia.
Blanchard, O. and Milesi-Ferretti, G. M., 2010, Global imbalances: in midstream?, in:
Blanchard, O. and SaKong, I., Reconstructing the world economy, International Monetary
Fund.
Blanchard, O. and Milesi-Ferretti, G. M., 2011. (Why) should global imbalances be reduced?
Staff Discussion Note 11/03, International Monetary Fund.
Blanchard, O. and Quah, D., 1989. The Dynamics Effects of Aggregate Demand and Supply
Disturbances. American Economic Review 79, 655-673.
Bluedorn, J. C. and Bowdler, C., 2010. The open economy consequences of U.S. monetary
policy. Journal of International Money and Finance 30, 309-336.
Caballero, R. J. and Krishnamurthy, A., 2009. Global imbalances and financial fragility.
American Economic Review 99, 584-588.
Caballero, R. J., Farhi, E. and Gourinchas, P.-O., 2008a. Financial crash, commodity prices,
and global imbalances. Brookings Papers on Economic Activity, 1-55.
Caballero, R. J., Farhi, E. and Gourinchas, P.-O., 2008b. An equilibrium model of global
imbalances. American Economic Review 98, 358-393.
Calvo, G., Leiderman, L., and Reinhart, C., 1994. The capital inflows problem: Concepts and
issues. Contemporary Economic Policy 12, 54-66.
Canova, F., 2005. The transmission of US Shocks to Latin America. Journal of Applied
Econometrics 20, 229-251.
Cashin, P. and McDermott C., 2002. Terms of Trade Shocks and the Current Account:
Evidence from Five Industrial Countries. Open Economies Review 13, 219-235.
16
Chen, B., 2007. An Empirical Review of Methods for Temporal Distribution and
Interpolation in the National Accounts. Working paper 0077. Bureau of Economic Analysis.
Chinn, M. and Ito, H., 2007. Current Account Balances, Financial Development and
Institutions: Assaying the World “Saving Glut”. Journal of International Money and Finance
26, 546-569.
Chinn, M. and Ito, H., 2008. A New Measure of Financial Openness. Journal of Comparative
Policy Analysis 10, 309-322.
Clarida, R. and Galì, J., 1994. Sources of Real Exchange Rate Fluctuations: How Important
are Nominal Shocks? NBER working paper 4658.
Clemente, J., Montanes, A. and Reyes, M., 1998. Testing for unit root in variables with a
double change in the mean. Economics Letters 59, 175-182.
Cooper, R., 2008. Global imbalances: globalization, demography, and sustainability. Journal
of Economic Perspectives 22, 93-112.
Cushman, D. O. and Zha, T. A., 1997. Identifying Monetary Policy in a Small Open Economy
Under Flexible Exchange Rates. Journal of Monetary Economics 39, 433-448.
Doan, T.A., 2010. RATS User’s Guide, version 8. Estima, Evanston, IL.
Dooley, M., Folkerts-Landau, D. and Garber, P., 2003. An Essay on the Revived Bretton
Woods System. NBER working paper 9971.
Dooley, M., Folkerts-Landau, D. and Garber, P., 2004. The revived Bretton Woods system.
International Journal of Finance and Economics 9, 307-313.
Eichengreen, B., 2006. Global Imbalances and the Asian Economies: Implications for
regional Cooperation. Working paper series on Regional Economic Integration 4. Asian
Development Bank.
Eichengreen, B., 2011. Exorbitant Privilege. Oxford University Press.
Elbourne, A. and de Haan, J., 2006. Financial structure and monetary policy transmission in
transition countries. Journal of Comparative Economics 34, 1-23.
Gagnon, J. E. and Chaboud, A. P., 2007. What Can the Data Tell Us about Carry Trades in
Japanese Yen? International Finance Discussion Papers 899. Federal Reserve System.
Gimet, C., 2011. The vulnerability of Asean+3 countries to international financial crises.
Review of International Economics 19, 894-908.
Giuliodori, M., 2004. Nominal Shocks and the Current Account: A Structural VAR Analysis
of 14 OECD Countries. Review of World Economics 140, 569-591.
Gossé, J.-B., and Guillaumin, C., 2010. The impact of external shocks on the eurozone: a
structural VAR model. Working paper 2010-03, King’s College, London.
17
Gourinchas, P.-O. and Rey, H., 2005. From world banker to world venture capitalist: US
external adjustment and the exorbitant privilege. NBER working paper 11563.
Gros, D., Mayer, T. and Ubide, A., 2006. A World Out of Balance? Special Report of the
CEPS Macroeconomic Policy Group, Brussels.
Guillaumin, C., 2009. Financial Integration in East Asia: Evidence from Panel Unit Root and
Panel Cointegration Tests. Journal of Asian Economics 20, 314-326.
Gupta-Kapoor, A. and Ramakrishnan, U., 1999. Is there a J-curve? A new estimation for
Japan. International Economic Journal 13, 71-79.
Henning, C., 2002. East Asian Financial Cooperation. The Peterson Institute for International
Economics.
Huang, Y. and Wang, B., 2010. Cost distortions and structural imbalances in China. China
and World Economy 18, 1-17.
Hutchinson, M. and Sushko, V., 2010. Impact of Macroeconomic Surprises on Carry Trade
Activity. Mimeo.
IEA, 2011. World Energy Outlook.
Ilzetzki, E., Reinhart, C., and Rogoff, K., 2009. Exchange Rate Arrangements Entering the
21st Century: Which Anchor Will Hold? Mimeo.
Jang, K. and Ogaki, M., 2001. The effects of monetary policy shocks on exchange rates: a
structural vector error correction model approach. Working paper 01-02. Department of
Economics, Ohio State University.
Kim, S. and Roubini, N., 2008. Twin deficit or twin divergence? Fiscal policy, current
account, and real exchange rate in the U.S. Journal of International Economics 74, 363-383.
Kinda, T., 2011. Modeling Inflation in Chad. IMF working paper 11/57.
Lane, P. R. and Milesi-Ferretti, G. M., 2007. The External Wealth of Nations Mark II. Journal
of International Economics 73, 223-250.
Lee, J. and Chinn, M. D., 1998. The current account and the real exchange rate: a structural
VAR analysis of major currencies. NBER working paper 6495.
Lee, J. and Chinn, M. D., 2006. Current account and real exchange rate dynamics in the G7
countries. Journal of International Money and Finance 25, 257-274.
Leeper, E., Sims, C. and Zha, T., 1996. What Does Monetary Policy Do? Brookings Papers
on Economic Activity 27, 1-63.
Levy-Yeyati, E. and Sturzenegger, F., 2005. Classifying Exchange Rate Regimes: Deeds vs.
Words. European Economic Review 49, 1603-1635.
18
Liu, Z., Wang, P. and Zha, T., 2011. Land-Price Dynamics and Macroeconomic Fluctuations.
Working paper 2011-26. Federal Reserve Bank of San Francisco.
Lustig, H. and Verdelhan, H., 2009. Discussion of carry trades and currency crashes. NBER
Macroeconomics Annual 23, 361-384.
Maćkowiak, B., 2007. External shocks, U.S. monetary policy and macroeconomic
fluctuations in emerging markets. Journal of Monetary Economics 54, 2512-2520.
Obstfeld, M. and Rogoff, K., 2004. The Unsustainable U.S. Current Account Position
Revisited. NBER working paper 10869.
Perron, P., 1989. The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis.
Econometrica 57, 1361-1401.
Rajan, R., 2010. Fault lines, Princeton University Press.
Reinhart, C. and Rogoff, K., 2004. The Modern History of Exchange Rate Arrangements: A
Reinterpretation. Quarterly Journal of Economics 119, 1-48.
Rüffer, R., Sànchez, M., and Shen, J.-G., 2007. Emerging Asia’s Growth and Integration.
How Autonomous are Business Cycles? Working paper 715. European Central Bank.
Sato, K., Zhang, Z. and McAleer, M., 2011. Identifying shocks in regionally integrated East
Asian economies with structural VAR and block exogeneity. Mathematics and Computers in
Simulation 81, 1353–1364.
Schindler, M., 2009. Measuring Financial Integration: A New Data Set. IMF Staff Papers 56,
222-238.
Sims, C. A., 1992. Interpreting the macroeconomic time series facts. The effects of monetary
policy. European Economic Review 36, 975-1000.
Sims, C. A., Stock, J. H., and Watson, M. W., 1990. Inference in Linear Time Series Models
with some Unit Roots. Econometrica 58, 113-144.
Sims, C. A. and Zha, T., 1999. Error bands for impulse responses. Econometrica 67, 1113-
1155.
White, W., 2006. Is Price Stability Enough? BIS working papers 205. Bank for International
Settlements.
WEF, 2010. The Financial Development Report.
Yip, P., 2008. Exchange Rate Systems and Policies in Asia. World Scientific Publishing.
Zha, T. A., 1999. Block Recursions and Structural Vector Autoregressions. Journal of
Econometrics 90, 291-316.
19
Zivot E. and Andrews D. W. K., 1992. Further Evidence on the Great Crash, the Oil Price
Shock and the unit Root Hypothesis. Journal of Business and Economic Statistics 10, 251-
270.
20
Appendix A
Data description
The data cover the period 1981:1 to 2010:3 for quarterly data. The sample includes the
following countries: China, Korea, Hong Kong, Indonesia, Japan, Malaysia, the Philippines,
Singapore and Thailand.
GDP (or, if unavailable, industrial production), real effective exchange rates and current
account balance data come from the IMF’s International Financial Statistics. For China,
Hong Kong, Malaysia and Singapore, as we have obtained only annual data for the current
account, we take the trade balance.
For China, GDP data and consumer price index come from the Datastream database. Real
effective exchange rate of Korea, Hong Kong, Indonesia, and Thailand come from the Bank
for International Settlements. These rates are monthly data transformed into quarterly data.
Oil price matches the Brent oil price taken from the IMF’s International Financial Statistics
and the database of EIA (Energy Information Administration). Real oil price is obtained by
deflating oil price using the World GDP deflator taken from the World Bank database. The
U.S. short-term interest rate (Fed Funds) and the S&P 500 index come from the database of
the Federal Reserve Bank of St Louis. Finally, OECD data from 30 member countries were
used to obtain global GDP.
Each of the estimated BVAR models includes a dummy crisis variable in order that the Asian
crisis may be considered. Its value is set to 1 for quarters 1997:2 and 1998:4, and to 0 the rest
of the time. We also introduce a dummy variable which is equal to 1 from 2008:3 to 2009:2
and 0 otherwise in order to take into account the effects of the world crisis started with the
subprime crisis.
21
Appendix B
Statistics
China Hong Kong SAR* Indonesia
Japan South Korea Malaysia
Philippines Singapore Thailand
Figure B.1: Asian current account balances (in % of GDP)
* No data are available before 1998. Source: World Bank.
Figure B.2: Decomposition of Asian current account balances (in % of total Asian GDP) Source: World Bank.
-4
-2
0
2
4
6
8
10
12
1985 1990 1995 2000 2005 20100
2
4
6
8
10
12
14
1985 1990 1995 2000 2005 2010-8
-6
-4
-2
0
2
4
6
1985 1990 1995 2000 2005 2010
0
1
2
3
4
5
1985 1990 1995 2000 2005 2010-15
-10
-5
0
5
10
15
20
1985 1990 1995 2000 2005 2010
-10
-8
-6
-4
-2
0
2
4
6
1985 1990 1995 2000 2005 2010-12
-8
-4
0
4
8
12
16
20
24
28
1985 1990 1995 2000 2005 2010-12
-8
-4
0
4
8
12
16
1985 1990 1995 2000 2005 2010
-2,0%
-1,0%
0,0%
1,0%
2,0%
3,0%
4,0%
5,0%
6,0%
7,0%
8,0%
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
China Hong Kong SAR, China Indonesia Japan Korea, Rep. Malaysia Philippines Singapore Thailand
22
Figure B.3: Contribution to the variation of Asian current account balances (in % of total Asian GDP)
Source: World Bank.
Table B.1: Asian indexes
Real indexes Monetary indexes Financial indexes
Oil balance on
total production of energy
Oil products
exports on total
production of energy
Degree of trade
openness
Bilateral trade
balance vis-à-vis
the US (% of GDP)
Exchange rate
flexibility
Degree of capital account
openness (KAOPEN)
Financial integration
index
Financial development
index
China -10% 1% 18% 3.5% 0% 12% 50% 4.03
Hong Kong -128% 7% 43% 3.7% 0% 100% 1111% 5.04
Indonesia 9% 3% 22% 2.7% 30% 86% 91% 2.9
Japan -61% 4% 10% 1.7% 100% 98% 90% 4.67
Korea -59% 20% 29% 2.2% 28% 33% 65% 4
Malaysia 48% 13% 67% 7.6% 53% 70% 158% 4.2
Philippines -34% 4% 32% 2.0% 59% 36% 113% 2.97
Singapore -301% 421% 101% 0.6% 46% 98% 506% 5.03
Thailand -34% 8% 36% 3.4% 60% 39% 95% 3.37 Notes: authors’ own calculations from IEA (2011) for production of energy, oil balance and oil products exports; CHELEM (CEPII) and IFS (IMF) for degree of trade openness and the bilateral trade balance vis-a-vis the US; Levy-Yeyati and Sturzenegger (2005) and Schindler (2009) for exchange rate flexibility; Lane and Milesi-Ferreti (2007) for financial integration index; WEF (2010) for financial development index; Chinn and Ito (2008) for degree of capital account openness.
Appendix C
Contribution of external shocks
-2,0%
-1,5%
-1,0%
-0,5%
0,0%
0,5%
1,0%
1,5%
2,0%
2,5%
3,0%
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
China Hong Kong SAR, China Indonesia Japan Korea, Rep. Malaysia Philippines Singapore Thailand Asie
23
Table C.1: Contribution of external shock to the variance of domestic variables
Variables Source of disturbance
Steps China Hong Kong Indonesia Japan Korea Malaysia Philippines Singapore Thailand
y / y* Shock 1 4 quarters 2 0 1 5 1 0 0 0 0 20 quarters 35 10 10 3 8 2 7 2 6 Shock 2 4 quarters 11 1 0 0 2 0 10 0 1 20 quarters 21 25 3 0 1 21 13 23 2 Shock 3 4 quarters 0 8 3 1 3 4 4 2 2 20 quarters 3 19 1 20 2 15 11 14 10 Sum 4 quarters 13 10 4 6 6 4 13 3 3 20 quarters 60 54 14 24 11 37 31 39 18
reer Shock 1 4 quarters 1 9 5 7 3 1 9 3 1 20 quarters 12 37 18 19 3 3 41 5 2 Shock 2 4 quarters 6 5 0 3 9 1 0 2 13 20 quarters 14 10 1 16 16 17 2 3 16 Shock 3 4 quarters 0 0 4 3 2 1 2 2 2 20 quarters 0 1 31 2 18 5 1 4 40 Sum 4 quarters 7 14 9 12 13 3 11 7 16 20 quarters 26 48 50 37 37 25 44 13 58
ca / y Shock 1 4 quarters 1 9 2 6 4 9 3 1 2 20 quarters 3 13 6 11 13 38 43 26 10 Shock 2 4 quarters 0 1 6 1 3 1 0 3 0 20 quarters 5 2 15 4 23 3 2 11 2 Shock 3 4 quarters 0 0 1 7 0 0 0 0 4 20 quarters 3 1 10 6 9 8 1 3 26 Sum 4 quarters 2 10 8 13 7 10 3 3 6 20 quarters 12 16 31 21 45 50 46 40 38
Notes: y / y*, reer and ca / y are, respectively, the growth differential, the real effective exchange rate and the current account balance in per cent of GDP. 4 quarters corresponds to the cumulative response after 4 quarters. 20 quarters corresponds to the cumulative response after 20 quarters.
24
Shock 1, shock 2 and shock 3 are, respectively, the oil shock, the US monetary shock and the US financial shock.
25
Appendix D
Impulse response functions
26
Figure D.1: Responses of domestic variables to the oil shock */ yy eerr yca /
China
Hong Kong
Indonesia
Japan
Korea
Malaysia
Philippines
Singapore
Thailand
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0 5 10 15-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0 5 10 15-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0 5 10 15-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0.03
0 5 10 15-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0 5 10 15-0.0175
-0.0150
-0.0125
-0.0100
-0.0075
-0.0050
-0.0025
0.0000
0.0025
0.0050
0 5 10 15-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0.03
0.04
0 5 10 15-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0 5 10 15-0.006
-0.004
-0.002
0.000
0.002
0.004
0.006
0 5 10 15-0.030
-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0 5 10 15-0.20
-0.15
-0.10
-0.05
-0.00
0.05
0.10
0.15
0.20
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0 5 10 15-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-1.00
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
0 5 10 15-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0 5 10 15-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0 5 10 15-0.05
-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0 5 10 15-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0 5 10 15-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0 5 10 15-0.020
-0.015
-0.010
-0.005
0.000
0.005
0 5 10 15-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
27
Figure D.2: Responses of domestic variables to the US monetary shock */ yy eerr yca /
China
Hong Kong
Indonesia
Japan
Korea
Malaysia
Philippines
Singapore
Thailand
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0 5 10 15-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0 5 10 15-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0 5 10 15-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0.03
0 5 10 15-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0 5 10 15-0.0175
-0.0150
-0.0125
-0.0100
-0.0075
-0.0050
-0.0025
0.0000
0.0025
0.0050
0 5 10 15-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0.03
0.04
0 5 10 15-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0 5 10 15-0.006
-0.004
-0.002
0.000
0.002
0.004
0.006
0 5 10 15-0.030
-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0 5 10 15-0.20
-0.15
-0.10
-0.05
-0.00
0.05
0.10
0.15
0.20
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0 5 10 15-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-1.00
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
0 5 10 15-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0 5 10 15-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0 5 10 15-0.05
-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0 5 10 15-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0 5 10 15-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0 5 10 15-0.020
-0.015
-0.010
-0.005
0.000
0.005
0 5 10 15-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
28
Figure D.3: Responses of domestic variables to the US financial shock */ yy eerr yca /
China
Hong Kong
Indonesia
Japan
Korea
Malaysia
Philippines
Singapore
Thailand
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0 5 10 15-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0 5 10 15-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0 5 10 15-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0.03
0 5 10 15-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0 5 10 15-0.0175
-0.0150
-0.0125
-0.0100
-0.0075
-0.0050
-0.0025
0.0000
0.0025
0.0050
0 5 10 15-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0.03
0.04
0 5 10 15-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0 5 10 15-0.006
-0.004
-0.002
0.000
0.002
0.004
0.006
0 5 10 15-0.030
-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0 5 10 15-0.20
-0.15
-0.10
-0.05
-0.00
0.05
0.10
0.15
0.20
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0 5 10 15-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-1.00
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
0 5 10 15-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0 5 10 15-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 5 10 15-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0 5 10 15-0.05
-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0 5 10 15-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0 5 10 15-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0 5 10 15-0.020
-0.015
-0.010
-0.005
0.000
0.005
0 5 10 15-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0 5 10 15-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
29
Table D.1: Contribution of external shocks to the explanation of Asian current account surpluses
Source of disturbance Horizon (quarters) China Hong Kong Indonesia Japan Korea Malaysia Philippines Singapore Thailand
Shock 1 4 quarters - (9) - (6) - (4) + (9) + (3) ns (1) ns (2) 20 quarters ns (13) + (11) + (13) + (38) + (43) + (26) + (10)
Shock 2 4 quarters - (6) + (3) - (3) 20 quarters + (15) + (23) + (11)
Shock 3 4 quarters ns (1) + (7) ns (0) ns (0) + (4) 20 quarters + (10) + (6) + (9) + (8) + (26)
Notes: positive (negative) signs indicate that the shock improves (deteriorates) the current account surplus. Contributions to the variance of the current account balance are in parentheses. Shocks 1, 2 and 3 are, respectively, the oil shock, the US monetary shock and the US financial shock.