Preliminary and incomplete
Please do not quote without permission
External Stability, Real Exchange Rate Adjustment, and the Exchange
Rate Regime1
Olivier Gervais
Bank of Canada
Lena Suchanek
Bank of Canada
Larry Schembri
Bank of Canada
May 2009
Abstract
Unsustainably large global current account imbalances are widely seen as an important
contributing factor to the ongoing financial crisis. The lack of exchange rate adjustment to
these imbalances is viewed as being partly responsible. Theories of external adjustment in open
economies (e.g., Obstfeld and Rogoff, 1994) maintain that smooth adjustment to external
imbalances can be most easily obtained via real exchange rate adjustment, rather than via
income/output/expenditure adjustment. The purpose of this paper is to explore two related
hypotheses: (i) that real exchange rate flexibility and adjustment is critical to maintaining
external stability and (ii) that a flexible nominal exchange rate facilitates real exchange rate
adjustment and the maintenance of external stability, if domestic wages and prices are sticky.
Based on an event study analysis for a large set of emerging market economies over the 19752008 period, we find that real exchange rate adjustment has contributed significantly to current
account adjustments. However, adjustment for fixed exchange rate regime countries with
current account deficits does not occur through the classical adjustment process, but as a
consequence of exchange rate crises, where the nominal exchange rate is forced to adjust and
the costs in terms of foregone GDP growth is high. Preliminary results using vector error
correction techniques support our finding in the event study; namely that the long run
relationship between the current account and the real exchange rate is negative, which is
consistent with theory.
JEL classification codes: F31, F32, F41
1
We would like to thank Wei Dong, Sharon Kozicki, Michael Francis, Robert Lavigne, and Jeannine Baillu for
helpful comments, and Firas Abu-Sneneh for excellent research assistance. The views in this paper are solely our
responsibility and should not be interpreted as reflecting the views of the Bank of Cananda.
Email: lsuchanek@bankofcanada.ca
1 Introduction
Unsustainably large global current account (CA) imbalances are widely seen as an important
contributing factor to the ongoing financial crisis. The lack of exchange rate adjustment to
these imbalances is viewed as being partly responsible.2 This concern about global imbalances
helped instigate the reform of IMF surveillance activities that resulted in the recent 2007
Decision on Surveillance (IMF 2007).
In this Decision, the IMF made the maintenance of “external stability” the focus of its
surveillance activities over members’ exchange rate, fiscal, monetary and financial sector
policies. The IMF refers to “external stability” as a balance of payments position that does not,
and is not likely to, give rise to disruptive exchange rate movements. Although the concept of
external stability encompasses both the current and the capital accounts of the balance of
payments because CA imbalances may not cause disruptive exchange rate movements if they
are complemented by sustainable capital inflows or outflows, the focus is primarily on CA
imbalances and real exchange rate (RER) misalignment. However, the fear of labeling member
country’s exchange rates as “misaligned” in Article IV reports has hampered the effective
implementation of the 2007 Surveillance Decision. Lavigne and Schembri (2009) argue that
the focus of IMF surveillance over exchange rates should, instead, be on permitting timely and
effective RER adjustment to ensure that CA imbalances are restored to sustainable levels.
Theories of external adjustment in open economies (e.g., Obstfeld and Rogoff, 1994) maintain
that smooth adjustment to external imbalances can be most easily obtained via RER
adjustment, rather than via income/output/expenditure adjustment. Relative price movements
cause expenditure to switch between domestic and foreign-produced goods and can take place
while the economy is operating at close-to-full employment. In contrast, income and
expenditure adjustment have proven to be much more disruptive. In theory, market-driven
RER adjustment will occur in response to a CA imbalance either via money supply and price
level movements under a fixed nominal exchange rate (i.e., the classical adjustment process) or
2
For example, the IMF writes in the “People’s Republic of China: Staff Report for the 2006 Article IV
Consultation”: “From a global perspective, exchange rate flexibility ... would also help contribute to an orderly
process for resolving global CA imbalances.” In addition, Garcίa-Herrero et Koivu (2007) find that a more
flexible nominal exchange rate would favour the adjustment of the trade balance.
Page 2 of 45
via movements in a flexible nominal exchange rate (i.e., the [Meade-] Friedman adjustment
process). Friedman (1953) and Borda (2005) maintain that because of the nominal stickiness of
domestic wages and prices, a flexible exchange rate can facilitate RER adjustment. Numerous
examples exist of the high cost of the classical adjustment process: the UK’s return to the gold
standard in 1926, the West German boom of the 1960s, and the Argentine experience with a
currency board in the 1990s. In practice, recent examples of the classical adjustment process
are rare. Countries with a fixed nominal exchange rate and CA surpluses frustrate the RER
adjustment process by sterilizing the impact on the domestic money supply. In countries with a
deficit, RER adjustment under a fixed exchange rate occurs as a consequence of an exchange
crisis in which the nominal rate is forced to adjust and economic activity is often disrupted.
The purpose of this paper is to explore two related hypotheses:
1. that RER flexibility and adjustment is critical to maintaining external stability (main
hypothesis) and
2. that a flexible nominal exchange rate facilitates RER adjustment and the maintenance
of external stability because nominal wages and prices are sticky (corollary hypothesis).
From a policy perspective, addressing these hypotheses and questions is important because
they speak directly to exchange rate policy and the choice of exchange rate regimes. First, does
a relatively flexible RER favor an adjustment of CA imbalances?3 And second, if this is so,
does a flexible exchange rate regime allow external imbalances to adjust more smoothly?
Although the existing literature has tended to focus on the second of our two hypotheses, that
is the choice of the nominal exchange rate regime, we believe it is important to establish the
validity of the first hypothesis, that is, the critical role of the RER in the adjustment process.
To examine these questions and hypotheses we adopt two complementary empirical
methodologies. We first examine the role of RER adjustments during periods of CA
adjustments using an event study for 21 emerging market economies (EMEs) from 1975-2008,
following the methodology of Freund and Warnock (2005). We also examine CA surplus
3
We measure external imbalances by the level of the CA. The literature has proposed alternative measures, such
as the level of external debt to GDP, foreign assets, or reserves in terms of import months. Adjustment capacity
has been measured by the change in imports following a shock (Igbar and Erbas, 1997) and the first order
autocorrelation of the CA (Cheung and Lai, 2007).
Page 3 of 45
reversals, and split the sample into crisis vs. non-crisis periods, and in fixed vs. floating
exchange rate regimes. To our knowledge, the question of exchange rate regimes has not been
addressed in the event study literature and this methodology seems to yield interesting results.
Our preliminary empirical findings are consistent with the two hypotheses .First, we find that
periods of CA reversals have been associated with sizeable RER depreciation. The larger the
CA reversal, the larger the depreciation. Second, there seems to be a tradeoff between the
adjustment that comes through the RER and through GDP. Put differently, if the RER can not
adjust (potentially due to a fixed nominal exchange rate and slow adjustment of price levels),
output/income has to take a larger burden in the adjustment. Third, the adjustment is more
painful in terms of output loss for countries that had a fixed exchange rate regime at the time of
the reversal, and the RER depreciates by more. Our results suggest that fast adjustment of
external imbalances can come through a flexible nominal ER, or, a crisis. Given that a crisis
comes along with higher cost in terms of output, this result suggests that a flexible ER allows a
rapid but orderly adjustment of the CA, which is consistent with our second hypothesis.
We then seek to examine the dynamic interaction of the CA, the RER, foreign and domestic
income for the same sample of EMEs using vector error correction techniques. To our
knowledge, there have been no attempts in literature to examine the long run relationship
between the CA and the RER. We find that there exists a negative long run equilibrium
cointegration relationship between the CA and the RER, which is consistent with theory. The
results moreover support our first hypothesis that the RER is an important factor in
determining the movement of the CA.
The paper proceeds as follows. Section 2 provides some theoretical background and a brief
literature review. Section 3 introduces the event study analysis and presents results. Section 4
specifies the VECM, and presents estimation results and analysis. Section 5 concludes.
2 Theoretical Background and Literature Review
The theory surrounding adjustment to CA imbalances in open economies has a long history,
starting most notably with the price-specific-flow adjustment mechanism under the gold
standard developed by David Hume in the 18th century. Since then there have been many
developments including the work of Viner (1937), Meade (1951), Friedman (1953), Mundell
Page 4 of 45
(1962) Dornbusch (1976), and Branson (1983), leading up to the Redux model of Obstfeld and
Rogoff (1995). In all of these models, the RER moves to facilitate adjustment to a CA
imbalance, regardless of whether the nominal exchange rate regime is permanently fixed,
flexible or intermediate. For example, following a shock to external demand that creates a CA
surplus, the RER will appreciate either through nominal exchange rate appreciation or through
a rise in domestic inflation, thus reducing competitiveness and slowing exports, while favoring
imports. The main channel through which RER helps bring about CA adjustment is through a
relative price change that causes an “expenditure-switching” effect captured by the IS curve in
the variations of the traditional Fleming-Mundell model. The “expenditure-switching”
mechanism retains its validity in the Obstfeld and Rogoff (1995) model provided that nominal
prices are fixed in the producer country and the exchange rate pass-through is complete.4
Milton Friedman (1953) argued in favor of flexible exchange rate regimes, based on the fact
that, in a world with sticky prices, the nominal exchange rate would adjust in response to
external real shocks and thus insulate the domestic economy. Friedman noted that in a world
with sticky prices the speed at which relative prices adjust depends crucially on the exchange
rate regime. Since then, a number of theories have confirmed his original intuition and it has
become one of the least disputed arguments in favor of flexible exchange rate regimes (see
Dornbusch (1980) for direct descendants of the open economy Mundell–Fleming models with
sticky prices; see Obstfeld and Rogoff (1996) and Corsetti and Pesenti (2001) for dynamic
general equilibrium models with nominal stickiness.)
In the Mundell-Fleming-Dornbusch model, floating exchange rates are superior to fixed
exchange rates when real shocks are the dominant source of disturbance to the economy.5
Under a fixed exchange rate regime, money demand shocks are fully and automatically
eliminated by foreign exchange rate intervention, thus leaving the real economy unaffected. In
contrast, following a real shock, domestic goods’ money prices have to bear the entire burden
4
Note that the J-curve implies that there might be an initial deterioration of the CA following a real depreciation
because the Marshall-Lerner condition may not hold in the short run. A real depreciation initially causes imports
to be more expensive and exports less expensive, and, if volumes are predetermined, will reduce the CA. After a
while, though, the volume of exports will start to rise because of their lower more competitive prices, causing the
demand for exports to pick up and domestic consumers to switch their expenditure to domestic products and away
from expensive imported goods and services. Eventually, the CA should rise.
5
See Obstfeld and Rogoff (1997)
Page 5 of 45
of downward adjustment. Because these are sticky, the economy reaches its new long-run
equilibrium only gradually. The opposite is true for floating exchange rate regimes: following a
real shock, the adjustment to a new equilibrium is immediate, reached by an appreciation of the
nominal exchange rate, which eliminates the need for a change in the nominal price level.
An empirical implication of these theories is that the adjustment of external imbalances across
exchange rate regimes should differ. In particular, regimes that allow for faster movements in
the RER should allow a faster adjustment to external imbalances such as large CA deficits.
Under a flexible regime, the RER and relative prices can adjust immediately through changes
in the nominal exchange rate, while under fixed regimes the changes happen at the rate
permitted by the nominal stickiness, which is usually much slower and often more painful in
terms of lost employment and output or much higher inflation (Dornbusch, 1980). Therefore,
flexible regimes should allow quicker relative price adjustments and thus a faster resolution of
CA imbalances.
Although theoretical models are based on RERs, there is limited evidence on our first
hypothesis. Only few researchers link the RER to the adjustment process of real variables (e.g.
Lee and Chinn, 2002). Arghyrou and Chortareas (2008) address the question of CA adjustment
and the role of the RER using a vector error correction model. In particular, the authors focus
on the diverging CAs of the individual euro area countries, dynamics of CA adjustment, and
the role of the RER. They find that the RER has a substantial impact on CA adjustment but that
this relationship subject to nonlinear effects.
Freund and Warnock (2005) use an event study approach to examine episodes of CA
adjustment in industrial countries. They first determine periods of CA deficit reversals using
criteria set up in Freund (2000), and then examine the behavior of key variables during the
reversal. Among other results, they find that CA adjustment tends to be associated with slow
income growth and a real depreciation.
With respect to the second hypothesis, the literature has suggested methods to classify
exchange rate regimes (e.g. Reinhart and Rogoff, 2004, and Levy-Yeyati and Sturzenegger,
Page 6 of 45
2005)6 and the importance of exchange rate regimes for external stability. Chinn and Wei
(2008), for instance, examine the importance of the nominal exchange rate regime for the
adjustment process of the CA and find there is no robust relationship between the exchange
rate regime and the rate of CA reversion.7 Moreover do not find a strong monotonic
relationship between flexibility of a nominal exchange regime and the speed of convergence in
RERs. However, these conclusions are somewhat misleading because they assume that
speedier adjustment is in all cases associated with less costly adjustment. In practice, CA
adjustment under fixed exchange rate regimes does not occur via the classical adjustment
process. Most often countries running CA deficits adjust through exchange rate crises or forced
devaluations which are associated with large employment and output losses. Moreover, their
reduced form regressions mask a large endogeneity problem and omit important control
variables, including the degree of wages and price flexibility and frequency and impact of
exchange rate crises.
Broda (2002) empirically assesses the Friedman hypothesis that a flexible nominal exchange
rate will help insulate domestic output to external real shocks. He analyzes the responses of
real GDP, RERs, and prices to terms of trade shocks and finds that responses are significantly
different across exchange rate regimes. He finds that countries with more flexible regimes tend
to have small real GDP losses and immediate large real depreciations. In contrast, countries
with fixed regimes experience large and significant declines in real GDP, and the RER
depreciates slowly by means of a fall in prices. Few authors even argue that the nominal
exchange rate regime does not matter at all.8
3 Empirical Methodology
To study the relationship between the CA and the RER, we first look at simple correlations.
Then, following Freund and Warnock (2005), we use an event study approach to assess the
6
Reinhart and Rogoff (2004), for instance, question the IMF classification and develop a system of reclassifying
historical exchange rate regimes, employing monthly data on market-determined parallel exchange rates. LevyYeyati and Sturzenegger (2002) construct a de facto classification based on data on exchange rates and
international reserves.
7
The authors use the results of Reinhart and Rogoff (2004) to determine the importance of the exchange rate
regime for the adjustment process of the CA.
8
For instance, Helpman (1981)
Page 7 of 45
behavior of the RER in periods of CA reversions. Lastly, we use VECMs to examine the role
of RER flexibility in CA adjustment.
3.1 Data and descriptive statistics
We use data on the RER, CA, and a number of control variables from a variety of sources
(listed in Table 1-3). Most data is quarterly.9 If only yearly data are available for a certain time
period, we calculate missing quarterly data using linear interpolation for RER and the CA. We
use the real effective exchange rate (REER) which is defined as the weighted average of the
indexed bilateral exchange rates (trade weighted with main trading partners), adjusted by
relative consumer prices.10 The RER is indexed to 100 for 2001:4, and an increase in the RER
corresponds to a real appreciation. Data availability reduces our pool to 21 EMEs; from 1975
to 2008.11 For comparison, we also look at the G7 countries for which data availability allows a
larger sample. Chart 1 provides an illustration of the CA and the RER for the countries
included in our sample. Consistent with our theoretical priors, CA reversals have generally
been associated with large exchange rate movements. For instance, Argentina’s CA reversed
from a deficit of about 3% in 2001Q1 to a surplus of 10% by the end of 2002, which was
accompanied by a RER depreciation of 60%. Likewise, the deterioration of Mexico’s CA from
a surplus of about 1.5% in 1987 to a deficit of 6% in 2003 was accompanied by a RER
appreciation of 78%.
Table 4 shows that the correlations between the main variables with the RER have the
expected sign. The correlation of the RER with the CA is negative – implying that an
appreciation is generally associated with a deterioration of the CA. This result is also
consistent with findings in the CA crisis literature. The correlation is more pronounced for
EMEs – we interpret the low correlation in G7 countries due to the fact that G7 countries’ CA
might be determined to a larger extent by other variables. The link between income growth and
the CA is not clear. In simple open economy models (e.g. Mundell-Fleming), and increase in
income is associated with higher spending on imported goods, and thus a deterioration of the
CA. We should thus expect a negative sign, however, this is only so for G7 countries. For
robustness, we also look at some control variables that have been used in the literature on CA
9
Quarterly data for the CA and the government balance has been seasonally adjusted.
For simplicity, we will continue denoting the real exchange rate by RER, referring to the REER.
11
The sample period is smaller for some countries due to missing data.
10
Page 8 of 45
determination. The government balance is positively correlated with the CA – mirroring the
fact that a fiscal expansion (i.e. a deterioration of the budget balance) could lead to higher
interest rates and in the presence of capital inflows an appreciation of the domestic currency
may occur which increases the CA deficit.
Chart 2 plots volatility of the CA vs. volatility of the RER – there seems to be a positive
relationship between the flexibility of the RER and the variability of the CA, suggesting some
evidence for our first hypothesis. To address the question whether RER flexibility is associated
with nominal exchange rate flexibility, we plot volatility of both in Chart 3. Nominal exchange
rate flexibility seems to be associated, to a reasonable degree, with RER flexibility. However,
the correlation between the two ranges from -.58 in Turkey to .99 in Malaysia (Chart 4). A
negagive correlation between the nominal exchange rate and RER movements implies that
nominal exchange rate movements are not trading closely differences in inflation rates as
relative purchasing power parity would suggest.
3.2 Event study approach
As mentioned above, theory suggests that CA improvements are associated with a RER
depreciation, and CA deterioration with RER appreciation. To examine the validity of this
hypothesis empirically, we first apply Freund and Warnock’s (2005) event study to our set of
EMEs. This kind of analysis is not new – for instance, Algieri and Bracke (2007) apply Freund
and Warnock’s methodology to a larger set of countries, including some EMEs. However, we
repeat and extend this exercise here for our set of countries to yield new insights about the role
of RER adjustment for CA adjustments for different groups of EMEs. In particular, we
distinguish between crisis and non crisis periods, and between fixed exchange rate regimes and
floating regimes at the time of the reversal. Moreover, we also analyze CA surplus reversals.
We then evaluate the behavior of output growth and RER movements, the two main
contributors to CA adjustment according to the existing literature (see Algieri and Bracke,
2007, for a summary) in periods of CA reversion.
In a first step, we determine CA reversal periods for our sample. The criteria for a CA reversal
are:
1) The CA deficit (surplus)-GDP ratio exceeded two percent before the reversal.
Page 9 of 45
2) The average deficit (surplus)-GDP ratio was reduced by at least two percentage points
over three years (from the minimum to the centered three year average).
3) The CA deficit (surplus)-GDP ratio was reduced by at least one third.
4) The maximum deficit (surplus)-GDP ratio in the five years after the reversal was not
larger than the minimum in the three years before the reversal.
The first three criteria ensure that large CA reversals are captured, whereas the fourth ensures
that the reversal was sustained, i.e. the change in the data was not only due to some heightened
volatility.
3.2.1 CA deficit reversals
Using these criteria on data for our set of EME countries from 1975-2008, we identify 55
episodes of CA deficit adjustment in 21 countries, listed in Table 5. Chart 5 documents the
pattern of adjustment across the CA, the RER, and GDP growth, with event time 0
corresponding to the year the CA balance is most negative. On average, the CA deficit stood at
-6.6% of GDP at the time of reversal, which is very close to the average reported by Freund
and Warnock (2005), although their sample only includes industrialized countries. In our
sample, there is considerable variation across episodes, ranging from relatively small deficits
(e.g. Argentina with 2% in 1980) to over 13% in Ecuador in 1998. During these periods, the
CA improved by an average of 6.2 percentage points within the 3 years following the start of
the episode.
In the majority of cases, patterns of real GDP growth and the RER movements are consistent
with theory and other findings of the literature: in 70% of the cases, the RER depreciated in the
three years following the start of the reversal. On average (for all identified periods), the RER
depreciated by 6.35%. However, in all 60 cases, countries experienced a total depreciation12
around the time of the CA reversal, of, on average, 31%. These results provide ample proof
that CA deficit reversals have been accompanied by currency depreciation, and that this
depreciation is large. This result is consistent with findings in the literature, although the
adjustment of the RER for EMEs seems to be more pronounced. Freund (2005) for instance
12
Total depreciation is defined as the percentage change between the maximum value of the RER in the three
years leading up to the reversal minus the minimum value of the RER in the three years following the reversal.
Page 10 of 45
finds that, in industrialized countries, CA deficit reversals are accompanied by a real
depreciation of about 10 to 20 percent. Moreover, there seems to be a positive correlation
between the size of the CA reversal (within 3 years) and the size of the total depreciation in
that period (Chart 6). Further, in 70% of all episodes, CA reversals have been accompanied by
a decrease in real GDP growth, by an average of 1.6 percentage points. The fall seems to be in
line with previous findings (Algieri and Bracke, 2007), and is consistent with theoretical priors.
We examine whether there is evidence of a trade off between adjustment though GDP or
through depreciation. Freund and Warnock (2005) argue that, restricted exchange rate
adjustment leads to lower income growth during CA recovery. In particular, income growth is
forced to accommodate CA adjustment precisely because RER depreciation is not sufficiently
large to facilitate the CA adjustment. Indeed, we also find an inverse correlation between the
extent of exchange rate adjustment and the slowdown in GDP growth (Chart 7). There is a
clear tradeoff: adjustment comes through either RER movements or GDP growth. If exchange
rates movements are limited, the CA position worsens further and the GDP reduction is more
extreme. Thus RER flexibility is critical to low-cost (in terms of output cost) CA adjustment.
Most of the countries in our sample have experienced currency and/or debt crises; these may
magnify the results.13 We therefore split the sample into crisis (26) and non-crisis (32) periods
(Chart 8).14 Not surprisingly, during crisis periods, the CA reverts faster and by more (7.9 pp
within 3 years), and is accompanied by a much stronger currency depreciation (15% in 3 years,
and 34% over the period as a whole). Also, GDP contracts substantially more during crisis
periods (2.54 pp).
To yield insight on our second hypothesis, we split the sample of CA deficit reversals into
floating and fixed exchange rate regimes at the time of the reversal. To classify episodes, we
use the classification compiled by Levy-Yeyati and Sturzenegger (2005) for the specific
countries at the time of the CA reversal. Their classification seems to better capture de facto
ER regimes compared to de jure regime classification such as in the IMF’s official ER
13
Note that the periods determined in table 5 include crisis periods as well as periods of “normal” reversion of CA
imbalances.
14
To determine crisis periods, we have to rely on several studies to determine crisis periods for all countries in our
sample: Frankel and Cavallo (2004) and Kaminsky (2003), Calvo and Reinhart (2000), and Calvo, Izquierdo, and
Mejía (2004). Crisis periods determined in different papers for the same country overlap significantly.
Page 11 of 45
classification, published annually in its Annual Report on Exchange Arrangements and
Exchange Restrictions.15 Some interesting results emerge (Chart 9): First, CA reversals occur
earlier in countries with fixed ER regimes, at a CA deficit to GDP ratio of 5.2% compared to
6.5% for floaters. Despite the deeper trough, the adjustment of the CA in floating ER regimes
occurs faster, suggesting that flexible ER regimes allow for faster resolution of external
imbalances. Second, significant RER depreciation precedes CA reversal in fixed exchange rate
regimes, with continuing sizeable depreciation during the reversal. This suggests that despite
the fixed nominal rate, adjustment has to come through the RER – in most cases through a
crisis and the collapse of the fixed ER regime. This adjustment through the RER is larger than
in the case for floaters. And most importantly, the cost in terms of GDP is significantly larger
in countries with fixed exchange rate regimes: GDP drops from an average of 8.2% two
quarters before the reversal to -.2% a year after the reversal, compared to a drop from 5.8 to
0.7% in floating regimes. This suggests that adjustment of external imbalances is more painful
given limited nominal ER flexibility. The results support our second hypothesis that flexible
ER regimes facilitate the maintenance of external stability through a rapid adjustment of
external imbalances. Moreover, countries with fixed exchange rate regimes only allow CA
adjustment through large depreciation and large losses in GDP growth, thus through much
more painful adjustment.
3.2.2 CA surplus reversals
Using the above mentioned criteria we determine 30 CA surplus reversal periods (Table 6).
The results are broadly symmetric to the analysis of CA deficit reversals. On average, the
surplus stood at 7.8% and fell by 8.9pp within 3 years (Chart 5). During these periods, the CA
improved by an average of 6.2 percentage points within the 3 years following the start of the
episode. Again, there is considerable variation across episodes, ranging from relatively small
surpluses (e.g. Bolivia with 2% in 1990Q4) to 20% in Russia in 2002Q2.
Within 3 years following the peak, the RER appreciated in 80% of the periods determined. The
average appreciation (including all periods) was large: 20% on average. In all episodes, the
RER appreciated in the 6 years around the reversal, by an average of 64%. Again, these results
15
For a discussion of de facto and de jure exchange rate regime classification, please refer to Bailliu, Lafrance and
Perrault (2003).
Page 12 of 45
provide ample evidence for our first hypothesis (that CA adjustments are facilitated by RER
movements), and that RER movements are quite large. Consistent with theory, GDP growth
increased during these periods (in 80% of all periods), by an average of 1.7pp (average over all
periods).
We can again split our sample into countries that followed a fixed exchange rate regime at the
time of the reversal, and those that let their currency float (Chart 10). 16 On average, CA
surpluses revert earlier in floating regimes (at 7% of GDP compared to 9% for fixers),
suggesting that fixed ER regime countries retard the adjustment of the CA. The major
contributing factor to CA reversion in fixed ER regime countries is rapid output growth –
suggesting that nominal ER rigidity impedes an adjustment through the RER. As for floaters,
both RER appreciation and GDP growth contribute to the adjustment process. The results seem
to again provide some evidence for our second hypothesis: the RER does not adjust in fixed
ER regimes; the adjustment has thus to come through GDP growth, and to a much larger extent
than for floaters.
3.3 Vector Error Correction Model approach
To analyze the long run relationship between the CA and the RER, we use a VECM approach
because the series are integrated, and the VECM allows the estimation of a cointegrating
relationship without imposing a causal relationship between two endogenous variables. The
main advantage of a VECM specification in the context of our research is that it allows
empirical estimation of the long run relationship between the CA and the RER, as well as to
16
Note that the sample size for this table is very small (4 fixed ER regimes and 10 floating ER regimes), and
drawing conclusions should be treated with respective caution.
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determine the reversion speed of the CA.17 A second advantage of the VEC model framework
is that it allows feedback effects between the variables.18
3.3.1 Integration and cointegration
A first step is to test for the stationary of the individual variables. In this paper, we apply the
standard Augmented Dickey-Fuller test to determine the level of integration. The results
(Tables 1-3) reject the hypothesis that the series are stationary, thereby implying that the
variables are, in general, I (1). In Bolivia, China, and Israel the RER is stationary.
The presence of unit roots leads us to test for cointegration. We use both the λ-max and the
Trace statistic to determine the number of cointegration relationships. Table 7 shows test
results. In 11 out of 22 cases we find evidence for one cointegration relationship. The
observation that the CA and the RER do not cointegrate for a number of countries can be
explained by several factors. First, for some countries, this might be due to the omitted variable
problem, an issue which we address in section 3.3.4. Second, the CA and RER might not adjust
as expected in theory because policies impede an adjustment of either variable. For instance, in
countries with fixed exchange regimes and sticky and/or regulated prices, the RER is likely to
be inflexible, and therefore would impede an adjustment of the RER to its long run equilibrium
value. This is likely the reason why China’s CA and RER do not cointegrate. Similarly, we
find no cointegration for Malaysia, India, and Thailand, all of which heavily intervene in their
currency markets. Sterilization may also be a reason why the CA does not adjust, as policies in
these countries do not allow a full adjustment of their RER.19 Given our theoretical priors, we
accept the evidence of one cointegrating vector for all our countries.
17
An alternative measure of the adjustment speed of the CA could be the size of the mean reverting coefficient
(e.g. Chinn and Wei 2008). However, the autoregressive coefficient on the CA might be influenced by the number
of shocks that a country experiences and therefore seems to fail to distinguish between rapid adjustments that
result from flexible policies as opposed to low persistence which results from the absence of shocks.
18
Note that a reduced form regression would estimate the effect of a depreciation of the exchange rate on the CA,
given by the partial derivative
derivative
19
∂CA
. The VECM allows to capture feedback effects, represented by the total
∂REER
dCA
.
dREER
See Lavigne (2008) “Sterilized Intervention in Emerging-Market Economies: Trends, Costs, and Risks”, Bank
of Canada Discussion Paper 2008-04.
Page 14 of 45
3.3.2 Specifying a Vector Error Correction Model (VECM)
We now specify a vector error correction model (VECM). A VEC model is a restricted VAR
designed for use with non-stationary series that are known to be cointegrated. The VECM has
cointegrating relations built into the specification so that it restricts the long-run behavior of
the endogenous variables to converge to their cointegrating relationships while allowing for
short-run adjustment dynamics. Given that the data is quarterly, the model is estimated with
three lags of each variable:
Equation 1
2
2
ΔCAt = α CA (CAt −1 − β RERt −1 ) + ∑ λ1,i ΔCAt −i + ∑ λ2,i ΔRERt −i + ε 1, t
i =1
i =1
2
2
i =1
i =1
ΔRERt = α RER (CAt −1 − β RERt −1 ) + ∑ λ3,i ΔRERt −i + ∑ λ 4,i ΔCAt −i + ε 1, t .
β specifies the long-run equilibrium relationship between the CA and the RER, whereas αCA
and αRER measure the speed of adjustment of the CA and the RER, respectively. In the long
run, the CA should return to the level compatible with the level of the RER:
CAt = β RERt
The cointegrating relationship is stationary and can be interpreted as an equation for the longterm relationship of the CA and the RER. We should expect β to be negative. A real
depreciation (a decrease in the RER) increases exports because of their lower more competitive
prices, causing the demand for exports to pick up and domestic consumers to switch their
expenditure to domestic products and away from expensive imported goods and services.
Table 8 gives the estimated cointegrating vectors for the countries, normalized on CAt.20
A negative value for αCA indicates that disequilibrium between the observed values for the CA
and its equilibrium moves observed CA toward the equilibrium value implied by the
cointegration relationship. The CA short-term variations can be more or less persistent
depending upon the value of αCA. For instance, a value of αCA near 0 will lead to very persistent
adjustment dynamics.
20
Given that the order of the variables in the VECM does not matter, we put the CA first, meaning that the
coefficient on in the cointegrating vector on the CA is 1.
Page 15 of 45
3.3.3 Results
We first examine the long-run relationship between the CA and the RER. The CA and the RER
are negatively cointegrated for 18 out of 22 cases (Table 7), and thus β has the expected sign
(Chart 11). The β coefficient on the RER is highly statistically significant in 13 out of 18 of
these cases. The results provide some evidence for the negative long term relation between the
CA and the RER, and thus support our hypothesis that RER movements are associated with
CA adjustment. The result is moreover consistent with findings by Arghyrou and Chortareas
(2008), although these authors focus only on European countries. This result holds despite the
fact that our sample includes periods of crisis – an interpretation of the negative coefficient is
that adjustment of the CA takes place through RER depreciation, whether it is as a result of
financial crisis or through a smooth adjustment. There is a wide dispersion in the size of the
coefficient: it ranges from 0.06 Brazil to 0.41 in Malaysia. In few cases, the sign of the
coefficient is counterintuitive: for instance, for China, the long term relation between the RER
and the CA is positive and statistically significant – which might be attributed to factors
mentioned above. This finding might also capture important aspects of China’s development
strategy of export-led growth. The lack of significance of the long run parameter for some
countries might be attributed to the fact that the RER on its own can not explain the reversion
of the CA – such as in Mexico. The inclusion of additional variables addresses this problem in
most cases (see next section). Moreover, there might be a lag in the volume adjustment in
response to a change in exchange rate, as stipulated by the J-curve, suggesting that the
contemporaneous relationship between the RER and the CA is small, but that the RER affects
the CA over time.
We now turn to the short run dynamics of our regression. Deviations from the CI relationship
can be corrected through the adjustment of the CA, and/or the adjustment of the RER. We
concentrate here only on the speed of adjustment of the CA, αCA:21 it is negative and
statistically significant in 20 out of 22 countries, indicating that the CA responds significantly
to the past deviations (Table 8, Chart 12). For the remaining two cases (Russia and China), the
21
The error-correction term in the RER equation, αRER, is not statistically significant for half of the countries, and
even positive in some cases, meaning that there is little evidence for an adjustment of the REER towards its longterm equilibrium value. This implies that for most countries, it is the CA that adjusts to the long run equilibrium,
whereas the REER is the trend variable.
Page 16 of 45
coefficient is not statistically significant. The fact that the coefficient is statistically significant
also implies that the RER Granger causes the CA in the long run, supporting our first
hypothesis.22 The level of the adjustment speed varies considerably across countries, ranging
from 0.005 in Bolivia to 0.295 in Korea. An α of 0.136 for Argentina, for instance, suggests
that 13.6% of the difference between the equilibrium and observed CA is eliminated within
one quarter. This corresponds to a half life of the CA of 4.74 quarters, or one year and 2
month. The average adjustment speed of 0.134 is comparable to Arghyrou and Chortareas’
(2008) findings, who find an average CA adjustment speed of 0.18 for 11 European countries,
ranging from 0.058 in France to 0.490 in Greece.
The interpretation of the size of this coefficient is not straightforward. Ignoring crisis periods, a
higher adjustment speed would speak to the ability of a country to adjust to external
imbalances. However, the adjustment of external imbalances can, and has in a large part of the
cases, come through crises. Therefore, the result that the CA adjusts rapidly might be driven by
the fact that these countries experienced CA crises with rapid movements in the RER and the
CA (such as in the case of Korea). For other countries the adjustment speed of the CA is quite
low. This observation could have several possible explanations. First, some countries have
limited nominal exchange rate flexibility. Thus, the absence of nominal exchange rate
adjustment may be depriving the country from a potent channel of CA adjustment. For these
countries, adjustment has to come through reducing relative unit labor costs taking the form of
competitive disinflations. If prices are rigid in the short run, convergence of the CA has to
come through other variables than the RER, such as higher income levels. These countries may
face significant CA imbalances in the event of demand shocks. Second, we may attribute slow
reversion of the CA to the fact that the CA adjusts through other variables not taken account,
which are more important in the reversion of the CA than the RER.
The adjustment speed of the CA does not seem to be positively correlated with the flexibility
of the de facto exchange rate regime. We compare the estimated error correction terms with the
22
Engle and Granger (1987) demonstrate that the results from the VECM model in Equation 1 can be used to test
causation from the RER to the CA – (that is, the RER does not Granger-cause the CA if all λ2,i and αCA = 0).
Besides, the VECM approach allows to distinguish between short-run and long-run Granger-causality. Long-run
Granger causality can be evaluated by testing whether the coefficient of the error-correction term (i.e. αCA) is
statistically different from zero by a t-test. The short run Granger causality can be evaluated by testing whether the
estimated coefficients on lagged values of ΔRERt in equation 1 are jointly statistically significant. However, we
are interested in the long run behaviour here and thus concentrate on the short run Granger causality test.
Page 17 of 45
exchange rate classification developed by Levy-Yeyati and Sturzenegger (2005) (Chart 13).
The average regime flexibility for a country is captured by the average over the estimation
period. Whereas Korea’s CA adjusts fairly quickly according to the estimated error correction
term, Levy-Yeyati and Sturzenegger classify this country as “intermediate” over the estimation
horizon. On the other hand, CA adjustment speed in Turkey is relatively slow when estimated
by the error correction term, although it has been classified as a floater for almost the whole
sample period. This is potentially due to the fact that the estimated coefficient αCA does not
distinguish rapid adjustment through crises or via a flexible nominal and real exchange rate.
Moreover, for some countries, the RER might not be the most important contributing factor to
CA adjustment, with other factors such as domestic GDP playing an important role.
3.3.4 Accounting for income growth differentials23
Theoretically, income growth differentials across countries should play an important role in the
determination of the CA. On the empirical side, Arghyrou and Chortareas (2008) estimate the
cointegrating relationship between the CA, the RER, and income growth differences.
Following Arghyrou and Chortareas (2008), we use G7 national income as a proxy for foreign
income. This measure certainly raises some concerns, as G7 national income might be better
suited as a variable of foreign demand for some countries more than others. However, we think
that it allows a preliminary assessment of the importance of foreign demand. We find that the
GDP, GDP* (foreign demand, i.e. G7 income), CA and the RER are cointegrated in 18 out of
21 countries (the exeptions being Brazil, Russia, and Thailand).24 In some cases, we can now
find a cointegration relationship where there was none when testing cointegration between the
CA and the RER, which suggests that the level of income growth is an important determinant
of CA reversion in these countries. Given our theoretical priors, we impose one cointegration
vector in the VECM.25
23
The literature has suggested a list of variables that could be cointegrated with the RER. We test for the budget
balance (cf. Afonso and Rault, 2008), terms of trade, and account for periods of crises using dummy variables.
24
Test results are not shown in order to save space.
25
Note that the log of GDP is stationary. However, Hansen and Juselius (1995) have noted that not all individual
variables included in the CI regression need be I (1). To find cointegration, between non-stationary variables, only
two of the variables have to be I(1). The system of variables should be chosen because of their economic
relevance, and not merely for their time series properties.
Page 18 of 45
Regarding the estimated cointegrating vectors (Table 9), in 13 out of 21 EMEs, the coefficients
on domestic national income have the expected sign and are statistically significant in most of
these cases.26 Evidence on the importance of foreign income is less evident: only in 12 out of
21 cases, the variable has the expected sign, of which only 7 are statistically significant.
Interestingly, with the exception of South Africa, the absolute values of the coefficients of
GDP and GDP* are lower than those of the RER, which is opposite to Arghyrou and
Chortareas’ (2008) findings. This suggests that for the EMEs under analysis, RERs have been
playing a more prominent role than relative incomes in long-run CA determination, whereas
for European countries, relative incomes seemed to have had a bigger impact on CA
determination. This result is moreover somewhat consistent with the findings in the event study
analysis, which suggests that the currency depreciates more significantly in EMEs than in
industrialized countries in order to facilitate CA adjustment.
4 Conclusion
The lack of exchange rate adjustment is widely seen as an important contributing factor to
unsustainably large global CA imbalances. In this paper we explore two related hypotheses: (i)
that RER flexibility and adjustment is critical to maintaining external stability (main
hypothesis) and (ii) that a flexible nominal exchange rate facilitates RER adjustment and the
maintenance of external stability (corollary hypothesis). We adopt two complementary
empirical methodologies. Using an event study analysis for a large set of EMEs over the 19752008 period, we find ample evidence in favor of our first hypothesis: RER adjustment is
critical to reducing CA imbalances and maintaining external stability. CA revisions are
accompanied by large RER movements, regardless of the exchange rate regime. However, the
crucial distinction becomes how countries adjust to large CA deficits - the adjustment may
come through an “orderly process” of gradual RER depreciation, or, through a crisis. Second,
we find evidence for our second hypothesis that a flexible nominal ER facilitates RER
adjustment and the maintenance of external stability. We find that 1) the adjustment is more
painful in terms of output loss in crisis periods and for countries that had a fixed exchange rate
regime at the time of the reversal, 2) the RER depreciates by more in crisis periods and for
26
The cointegrating relation coefficient on the RER remains relatively robust in most cases with respect to the
specification with two variables.
Page 19 of 45
countries with fixed exchange rate regimes, and 3) the CA reverts faster in crises and for
floating regimes. The results suggest that fast adjustment of external imbalances can come
through a flexible nominal ER, or, a crisis. Given that crises come along with higher cost in
terms of output, this result suggests that a flexible ER allows a rapid but orderly adjustment of
the CA which is comparatively less harmful in terms of output cost. Similarly, for CA surplus
reversals, the adjustment of the CA in fixed exchange rate regimes has to come through large
GDP growth movements, due to limited RER movement, whereas in floating exchange rate
regimes both real GDP growth and RER depreciation contribute to the adjustment process. The
result is again in line with our second hypothesis: a flexible ER regime facilitates the
adjustment of the RER, and thus of the CA. Of course, these empirical findings must be
interpreted with caution, as the CA, GDP, and the RER might be affected through other
variables that are omitted from the analysis and could explain the differences between
adjustments in fixed and flexible ER regime countries.
Our vector error correction modeling confirms that the negative relationship between the RER
and the CA holds in the long run. The result is consistent with our first hypothesis that CA
adjustment is correlated with RER movements, regardless of the ER regime. Deviations from
the long run relationships are moreover important drivers of CA reversion.
In sum, our findings support recent arguments that EMEs should permit their RERs to adjust to
external imbalances by eschewing efforts to sterilize the impact of these imbalances on the
domestic money supply and on domestic prices and wages. EMEs could allow more nominal
exchange rate flexibility in order to promote adjustment to external imbalances, thus avoiding
crises and large costs in terms of output.
Going forward, it would be interesting to separately identify the contribution of nominal
exchange rate and price adjustment to RER flexibility, and thus CA reversion. (…). Second,
our VECM analysis is based on a list of assumptions: For example, we assume exchange rate
regimes to be constant over the estimation period, which is arguably too simple. It would be
interesting to test for structural breaks in the estimated cointegration relationships to account
for the fact that estimated coefficients change pre- and post-crisis, or before and after a country
changes nominal exchange rate regime. Fujii (2002) studies a related question on PPP and
argues that long run parameters in the cointegration relation do not change, but that the
Page 20 of 45
adjustment speed of some Asian countries changed after the respective crisis. We could follow
his methodology to allow for a shift of alpha in crisis periods. Also, it could be insightful to
construct panel VECMs for our sample, grouping countries according to region and other
similarities (commodity exporters/importers, EME versus G7 countries, etc.). Panels allow a
more robust estimation of relevant coefficients, as the number of observation increases.27
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Relationship – A Panel Data Diagnostic.”ECB Working Paper No. 961 / November
2008
Algieri, Bernardina and Thierry Bracke. 2007. “Patterns of CA Adjustment – Insights from
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Arghyrou, Michael G. and Georgios Chortareas. 2008. “CA Imbalances and Real Exchange
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Bailliu, Jeannine, Robert Lafrance and Jean-Francois Perrault. 2003. "Does Exchange Rate
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Broda, Christian. 2004. “Terms of Trade and Exchange Rate Regimes in Developing
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Chinn, Menzie D. and Shang-Jin Wei. 2008. “A Faith-based Initiative: Does a Flexible
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27
However, Arghyrou and Chortareas (2008) argue that panel regressions can hide important differences in the
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Clarida, Richard, 2007, “G7 CA Imbalances: Sustainability and Adjustment?” University of
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Page 23 of 45
Annex
4.1 Tables
Table 1 CA Data
Country
Argentina
Bolivia
Brazil
Canada
Chile
China
Columbia
Czech Republic
Germany
Ecuador
France
United Kingdom
Hungary
Indonesia
India
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
1976-1992
Annual
World Bank
1977-1994
Annual
World Bank
1975-1990
Annual
World Bank
1970-2008
Quarterly
IMF IFS
1975-1995
Annual
World Bank
1980-2005
Annual
IIF
1970-1995
Annual
World Bank
1990-1992
Annual
IIF
1971-2008
Quarterly
IMF IFS
1980-1992, 20032008
Annual
IIF
1975-2008
Quarterly
IMF IFS
1970-2008
Quarterly
IMF IFS
1980-1994
Annual
IIF
1980-1989
Annual
IIF
1975-1995
Annual
World Bank
Page 24 of 45
1992-2008
Quarterly
IMF IFS
1995-2008
Quarterly
IMF IFS
1991-2008
Quarterly
IMF IFS
Integration
I(1) at 1%
I(1) at 1%
I(1) at 1%
I(1) at 1%
1996-2008
Quarterly
IMF IFS
2006-2008
Quarterly
IMF IFS
1996-2008
Quarterly
IMF IFS
1993-2008
Quarterly
IMF IFS
I(1) at 1%
I(1) at 5%
I(1) at 1%
I(1) at 1%
I(1) at 1%
1993-2002
I(0) at 1%
Quarterly
IMF IFS
I(1) at 1%
I(0) at 5%
1995-2008
Quarterly
IMF IFS
1990-2008
Quarterly
IMF IFS
1996-2008
Quarterly
IMF IFS
I(1) at 1%
I(1) at 1%
I(0) at 1%
Israel
Italy
Japan
Korea
Malaysia
Mexico
Peru
Philippines
Poland
Russia
Thailand
Turkey
United States
South Africa
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
1970-1971
Annual
World Bank
1971-2008
Quarterly
OECD
1977-2008
Quarterly
IMF IFS
1976-2008
Quarterly
IMF IFS
1975-1998
Annual
World Bank
1981-2008
Quarterly
IMF IFS
1977
Annual
World Bank
1977-2008
Quarterly
IMF IFS
1980-1999
Annual
IIF
1992-1996
Annual
IIF
1975-1992
Annual
World Bank
1974-1986
Annual
World Bank
1973-2008
Quarterly
IMF IFS
1970-2008
Quarterly
IMF IFS
Page 25 of 45
1972-2008
Quarterly
IMF IFS
I(1) at 1%
I(1) at 1%
I(0) at 5%
I(0) at 5%
1999-2008
Quarterly
IMF IFS
I(1) at 1%
I(0) at 5%
1978-2008
Quarterly
IMF IFS
I(1) at 1%
I(1) at 1%
2000-2008
Quarterly
IMF IFS
1997-2008
Quarterly
IMF IFS
1993-2008
Quarterly
IMF IFS
1987-2008
Quarterly
IMF IFS
I(0) at 5%
I(1) at 1%
I(1) at 1%
I(1) at 5%
I(1) at 1%
I(1) at 1%
Table 2: RER data
Country
Argentina
Bolivia
Brazil
Canada
Chile
China
Columbia
Czech Republic
Germany
Ecuador
France
United Kingdom
Hungary
Indonesia
India
Israel
Italy
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
1980-1993
Annual
IIF
1980-2008
Quarterly
IMF IFS
1980-1993
Annual
IIF
1975-2008
Quarterly
IMF IFS
1980-2008
Quarterly
IMF IFS
1980-2008
Quarterly
IMF IFS
1980-2008
Quarterly
IMF IFS
1980-1989
Annual
IIF
1975-2008
Quarterly
IMF IFS
1980-2008
Quarterly
IMF IFS
1980-2008
Quarterly
IMF IFS
1975-2008
Quarterly
IMF IFS
1980-2008
Quarterly
IMF IFS
1980-1993
Annual
IIF
1980-1993
Annual
IIF
1975-2008
Quarterly
IMF IFS
1980-2008
Quarterly
IMF IFS
Page 26 of 45
1994-2008
Quarterly
BIS
Integration
I(1) at 1%
I(0) at 1%
1994-2008
Quarterly
BIS
I(1) at 1%
I(1) at 1%
I(1) at 1%
I(0) at 5%
I(1) at 1%
1990-2008
Quarterly
IMF IFS
I(1) at 1%
I(0) at 1%
I(1) at 1%
I(0) at 10%
I(1) at 1%
I(1) at 1%
1994-2008
Quarterly
IMF IFS
1994-2008
Quarterly
IMF IFS
I(1) at 1%
I(1) at 1%
I(0) at 1%
I(1) at 1%
Japan
Korea
Malaysia
Mexico
Morocco
Peru
Philippines
Poland
Russia
Thailand
Turkey
United States
South Africa
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
1980-2008
Quarterly
IMF IFS
1980-1993
Annual
IIF
1975-2008
Quarterly
IMF IFS
1980-1993
Annual
IIF
1980-2008
Quarterly
IMF IFS
1970-2008
Quarterly
IMF IFS
1975-2008
Quarterly
IMF IFS
1980-2008
Quarterly
IMF IFS
1981-1993
Annual
IIF
1994-2008
Quarterly
IMF IFS
1994-2008
Quarterly
IMF IFS
1980-2008
Quarterly
IMF IFS
1975-2008
Quarterly
IMF IFS
I(1) at 1%
1994-2008
Quarterly
IMF IFS
I(1) at 1%
I(1) at 1%
1994-2008
Quarterly
IMF IFS
I(1) at 1%
I(0) at 1%
I(1) at 1%
I(1) at 1%
I(1) at 1%
1994-2008
Quarterly
IMF IFS
I(1) at 1%
I(1) at 1%
I(1) at 1%
I(1) at 1%
I(1) at 1%
Table 3 : GDP by Country
Country
Argentina
Bolivia
Brazil
Time Frame
1981-1990
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Annual
IMF IFS
1970-2008
Annual
IMF IFS
1970-1990
Annual
IMF IFS
Page 27 of 45
1970-1980, 19912008
Quarterly
IMF IFS
Integration
I(1) at 1%
I(0) at 1%
1991-2008
Quarterly
IMF IFS
I(1) at 1%
Canada
Chile
China
Columbia
Czech Republic
Germany
Ecuador
France
United Kingdom
Hungary
Indonesia
India
Israel
Italy
Japan
Korea
Malaysia
Mexico
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
1970-2008
Quarterly
OECD
1970-1979
Annual
IMF IFS
1970-2008
Annual
IMF IFS
1970-2008
Annual
IMF IFS
1995-2008
Quarterly
IMF IFS
1970-2008
Quarterly
OECD
1970-2008
Annual
IMF IFS
1970-2008
Quarterly
OECD
1970-2008
Quarterly
OECD
1970-1995
Annual
IMF IFS
19700-1993
Annual
IMF IFS
1970-2000
Annual
IIF
1970-2008
Quarterly
IMF IFS
1970-2008
Quarterly
OECD
1970-2008
Quarterly
OECD
1970-2008
Quarterly
IMF IFS
1970-1988
Annual
IMF IFS
1970-1980
Annual
Page 28 of 45
I(1) at 1%
1980-2008
Quarterly
IMF IFS
I(1) at 1%
I(0) at 5%
I(1) at 1%
I(1) at 1%
I(0) at 1%
I(1) at 1%
I(0) at 10%
I(1) at 1%
1996-2008
Quarterly
IMF IFS
1994-2008
Quarterly
IMF IFS
2001-2008
Quarterly
IMF IFS
I(1) at 1%
I(1) at 1%
I(1) at 1%
I(0) at 1%
I(1) at 1%
I(1) at 1%
I(1) at 1%
1989-2008
Quarterly
IMF IFS
1981-2008
Quarterly
I(1) at 1%
I(1) at 1%
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Time Frame
Frequency
Source
Morocco
Peru
Philippines
Poland
Russia
Thailand
Turkey
United States
South Africa
IMF IFS
1970-2008
Annual
IMF IFS
1970-2008
Annual
IMF IFS
1970-1981
Annual
IMF IFS
1981-1995
Annual
IMF IFS
1996-2008
Quarterly
IMF IFS
1970-1983
Annual
IMF IFS
1970-1987
Annual
IMF IFS
1970-2008
Quarterly
IMF IFS
1970-2008
Quarterly
IMF IFS
IMF IFS
I(0) at 1%
I(1) at 1%
1982-2008
Quarterly
IMF IFS
1996-2008
Quarterly
IMF IFS
I(1) at 1%
I(1) at 1%
I(1) at 1%
1984-2008
Quarterly
IMF IFS
1988-2008
Quarterly
IMF IFS
I(1) at 1%
I(1) at 1%
I(1) at 1%
I(1) at 1%
Table 4: Correlations
CA
RER
GG
GDP
All countries -0.1026
0.132059 0.022486
G7
-0.01851 0.172019 -0.01768
EMEs
-0.0969
0.130847 0.052265
CA stands for CA, RER for the real effective exchange rate, and GG for government balance.
Table 5 : List of CA deficit adjustment episodes and main characteristics
Country
Date
Started
Argentina
Argentina
Bolivia
Bolivia
Bolivia
Brazil
Brazil
1980Q4
1998Q1
1981Q4
1987Q4
1998Q2
1982Q4
2001Q1
CA Deficit
At
Beginning
-2.05
-5.16
-7.92
-9.29
-9.81
-5.77
-4.71
Change in
CA (3 Years)
Change in
RER from t0
to t3
2.51
1.95
5.75
11.34
5.81
5.57
6.03
Page 29 of 45
-66.34
8.70
99.65
-20.27
0.77
-35.00
-16.72
Change in
ER (Max to
Min)
Change in
Average GDP
Growth
-66.34
-16.02
-69.47
-88.51
-15.04
-35.00
-57.05
-3.79
-3.84
-3.99
4.60
-1.95
-0.50
0.45
Chile
Chile
China
Columbia
Columbia
Czech Rep.
Czech Rep.
Ecuador
Ecuador
Ecuador
Ecuador
Hungary
Hungary
Hungary
Indonesia
Indonesia
Indonesia
India
India
Israel
Israel
Israel
S. Korea
S. Korea
S. Korea
Malaysia
Malaysia
Mexico
Mexico
Mexico
Peru
Peru
Peru
Philippines
Philippines
Poland
Poland
Poland
Russia
Thailand
Thailand
Thailand
Turkey
Turkey
Turkey
S. Africa
S. Africa
S. Africa
1984Q2
1997Q4
1985Q4
1983Q1
1997Q4
1997Q1
2003Q4
1980Q4
1987Q4
1998Q3
2001Q2
1978Q4
1987Q1
1994Q3
1983Q4
1995Q4
2004Q1
1990Q4
1998Q1
1985Q1
1994Q2
2000Q4
1980Q1
1991Q1
1997Q1
1982Q4
1995Q4
1981Q4
1994Q4
2001Q1
1981Q4
1995Q4
1997Q3
1982Q4
1997Q4
1978Q4
1993Q4
1999Q4
1998Q1
1983Q2
1995Q3
2005Q2
1980Q4
1986Q1
1993Q4
1976Q1
1982Q1
1997Q1
-7.90
-7.91
-4.10
-7.85
-5.45
-8.19
-7.89
-5.43
-10.68
-13.89
-4.25
-7.55
-5.56
-10.50
-8.36
-2.68
-3.21
-3.02
-2.79
-5.41
-7.69
-3.31
-10.99
-3.67
-5.20
-13.24
-9.56
-6.44
-7.06
-3.13
-6.69
-8.84
-6.29
-8.64
-6.13
-10.84
-4.70
-7.38
-4.56
-4.99
-10.06
-8.34
-6.03
-2.06
-3.26
-8.72
-9.53
-2.34
3.26
5.20
2.87
4.44
6.52
3.93
4.46
4.51
7.19
11.70
2.91
4.37
1.97
6.62
4.19
6.46
5.56
2.60
2.54
5.47
4.06
4.80
5.50
2.87
7.07
11.22
23.42
8.85
5.15
2.45
7.07
5.40
4.93
8.49
2.20
4.85
2.58
4.79
20.25
3.30
20.85
12.45
2.25
3.56
2.25
15.15
11.52
1.59
Page 30 of 45
-38.90
-11.53
-14.42
-35.22
-17.09
4.62
14.35
2.46
0.30
-6.44
15.33
-9.18
-0.71
4.75
-22.25
-34.91
13.81
-8.00
0.89
-2.08
10.19
-17.95
-1.82
-5.23
-12.36
-4.09
-20.63
-27.93
-4.60
-8.15
2.34
-1.49
-7.74
-15.02
-17.73
-15.66
22.76
15.33
-26.43
-16.14
-12.72
19.34
-10.83
-15.95
10.40
17.27
-19.82
-11.21
-56.07
-14.20
-53.39
-37.13
-27.93
-22.60
-24.04
-19.37
-57.38
-50.34
-54.17
-3.31
-22.73
-14.59
-25.39
-64.43
-38.14
-12.09
-11.09
-18.66
-11.91
-20.79
-4.60
-19.20
-39.96
-17.30
-27.81
-38.15
-47.55
-29.48
-26.66
-16.05
-12.99
-29.15
-32.76
-38.28
-47.73
-27.55
-44.89
-17.00
-34.74
-18.30
-11.62
-25.78
-27.50
-19.91
-27.93
-24.27
7.00
-5.24
-1.47
4.33
-5.22
-8.98
2.53
-3.99
0.13
-2.64
4.81
-3.31
1.83
6.73
-1.17
-6.59
0.87
-2.34
-1.18
3.27
-0.20
-4.13
-4.60
-1.67
-5.01
-2.22
-5.79
-8.40
-1.06
-4.21
-6.42
-2.41
-5.49
-7.82
-2.60
0.05
10.94
-2.21
4.90
0.76
-0.45
-4.17
2.45
-0.17
-9.48
-2.16
-4.23
-1.68
Average
Crisis
Non crisis
-6.67
-6.62
-6.74
6.26
7.90
5.44
-6.35
-15.24
1.32
-31.17
-34.27
-28.15
-1.58
-2.54
-0.86
Table 6: List of CA surplus adjustment episodes and main characteristics
Arg
Bol
Bra
Chn
Col
Cze
Ecu
Idn
Ind
Isr
Kor
Mal
Mex
Per
Phl
Pol
Rus
Thl
Zaf
Average
28
28
1989Q4
2002Q3
1990Q4
2004Q2
1991Q1
1997Q4
1991Q4
1991Q4
2000Q1
2001Q1
2004Q2
1985Q3
1989Q3
1976Q3
1988Q1
1998Q1
2004Q1
1987Q4
1999Q3
1983Q4
1987Q4
1979Q4
1986Q4
1990Q4
2000Q2
2005Q2
1998Q1
2001Q2
1980Q1
1987Q3
Identified
CA
6.56
11.25
2.05
2.06
3.29
3.91
5.21
5.29
14.55
4.78
3.08
16.63
2.59
6.96
9.15
15.72
4.68
7.94
17.68
3.96
3.06
4.61
3.13
5.66
20.25
12.49
14.35
5.56
11.90
6.78
0.08
Deterioration
Over 3 Years
in CA
-8.38
-7.04
-9.12
-1.30
-4.40
-2.19
-9.81
-8.29
-19.22
-7.99
-4.62
-20.62
-6.01
-19.96
-12.82
-12.88
-4.05
-9.95
-10.89
-5.00
-5.89
-10.83
-6.61
-10.36
-12.20
-5.91
-11.68
-2.82
-10.13
-6.70
-0.09
Percent
Appreciation
from peak
to Year 3
143.11
14.99
5.34
54.56
-23.38
-1.23
41.75
25.24
117.52
29.62
9.07
9.60
2.99
-36.54
21.61
32.36
23.69
-11.90
3.99
-14.36
38.34
9.00
1.12
54.43
33.47
23.29
9.13
2.15
12.82
-3.44
19.79
Excluding the Poland 1990 period which significantly amplifies the averages.
Page 31 of 45
Total
appreciation
around 6
year range
143.11
169.66
28.61
89.17
108.70
33.02
46.99
74.50
118.20
113.67
14.23
22.94
24.87
104.63
28.38
66.56
30.15
55.65
38.53
88.78
49.59
41.68
49.30
1277.67
81.44
51.97
53.23
14.00
52.77
58.17
63.88
Change in
Growth Rates
(Before/After)
3.68
11.33
0.51
2.52
3.96
-2.70
0.84
1.85
6.64
3.52
1.80
1.48
-17.42
-0.53
-3.25
0.05
6.51
3.62
-1.61
3.52
3.75
9.82
-3.28
4.37
0.92
0.01
6.07
0.90
1.68
1.92
Table 7: Cointegration test (CA and RER)29
Trace Test
Hypothesized
Trace
No. of CE(s)
Eigenvalue
ARG
Statistic
Maximum Eigenvalue Test
0.05
Hypothesized
Critical Value
Prob.**
No. of CE(s)
Max-Eigen
Eigenvalue
Statistic
0.05
Critical Value
Prob.**
None *
0.108162
16.59607
15.49471
0.0340
None
0.108162
12.24831
14.26460
0.1017
At most 1 *
0.039819
4.347752
3.841466
0.0371
At most 1 *
0.039819
4.347752
3.841466
0.0371
BOL
None
0.086803
9.814474
15.49471
0.2953
None
0.086803
9.715976
14.26460
0.2313
BRA
None *
0.107752
18.63310
15.49471
0.0163
None
0.107752
12.19915
14.26460
0.1034
At most 1 *
0.058358
6.433942
3.841466
0.0112
At most 1 *
0.058358
6.433942
3.841466
0.0112
None *
0.080075
16.37675
15.49471
0.0368
None
0.080075
9.097476
14.26460
0.2781
At most 1 *
0.064601
7.279275
3.841466
0.0070
At most 1 *
0.064601
7.279275
3.841466
0.0070
None
0.051787
7.888151
15.49471
0.4774
None
0.051787
5.796187
14.26460
0.6395
At most 1
0.019009
2.091964
3.841466
0.1481
At most 1
0.019009
2.091964
3.841466
0.1481
CHL
CHN
COL
None *
0.151617
24.32501
15.49471
0.0018
None *
0.151617
17.92208
14.26460
0.0126
At most 1 *
0.057050
6.402937
3.841466
0.0114
At most 1 *
0.057050
6.402937
3.841466
0.0114
CZE
None
0.077627
10.02835
15.49471
0.2786
0.077627
8.565447
14.26460
0.3241
ECU
None *
0.141142
21.35864
15.49471
0.0058
None *
0.141142
16.58448
14.26460
0.0211
At most 1 *
0.042854
4.774161
3.841466
0.0289
At most 1 *
0.042854
4.774161
3.841466
0.0289
None *
0.117021
15.81189
15.49471
0.0448
None
0.117021
13.56549
14.26460
0.0642
At most 1
0.020398
2.246394
3.841466
0.1339
At most 1
0.020398
2.246394
3.841466
0.1339
None
0.094882
15.11987
15.49471
0.0569
None
0.094882
10.66685
14.26460
0.1717
At most 1 *
0.040763
4.453027
3.841466
0.0348
At most 1 *
0.040763
4.453027
3.841466
0.0348
IND
None
0.080748
13.87162
15.49471
0.0865
None
0.080748
9.008911
14.26460
0.2854
ISR
None *
0.147005
25.53496
15.49471
0.0011
None *
0.147005
20.51125
14.26460
0.0045
At most 1 *
0.038195
5.023714
3.841466
0.0250
At most 1 *
0.038195
5.023714
3.841466
0.0250
None *
0.118933
18.85853
15.49471
0.0150
None
0.118933
13.54848
14.26460
0.0646
At most 1 *
0.048415
5.310043
3.841466
0.0212
At most 1 *
0.048415
5.310043
3.841466
0.0212
None
0.045854
7.524112
15.49471
0.5176
None
0.045854
5.257178
14.26460
0.7091
HUN
IDN
KOR
MAL
MEX
PER
None *
0.201539
28.14199
15.49471
0.0004
None *
0.201539
24.08243
14.26460
0.0011
At most 1 *
0.037229
4.059562
3.841466
0.0439
At most 1 *
0.037229
4.059562
3.841466
0.0439
None
0.095776
14.03171
15.49471
0.0821
None
0.095776
11.57799
14.26460
0.1275
At most 1
0.021111
2.453717
3.841466
0.1172
At most 1
0.021111
2.453717
3.841466
0.1172
PHL
None
0.058825
8.907372
15.49471
0.3740
None
0.058825
6.911344
14.26460
0.4997
POL
None
0.067944
11.88509
15.49471
0.1625
None
0.067944
7.669508
14.26460
0.4133
RUS
None *
0.237985
22.18319
15.49471
0.0042
None *
0.237985
17.93810
14.26460
0.0126
At most 1 *
0.062295
4.245087
3.841466
0.0394
At most 1 *
0.062295
4.245087
3.841466
0.0394
THL
None
0.094292
14.36123
15.49471
0.0735
None
0.094292
10.59712
14.26460
0.1756
TUR
None
0.087939
9.850046
15.49471
0.2924
None
0.087939
9.849138
14.26460
0.2220
ZAF
None
0.093926
14.42595
15.49471
0.0720
None
0.093926
12.72384
14.26460
0.0863
29
Shaded areas indicate evidence of a cointegration relationship.
Page 32 of 45
Table 8: Cointegration results (CA and log(RER))30
Arg
Bol
bra
chl
chn
col
cze
ecu
Hun
idn
ind
isr
kor
mal
α_ca
-0.13607***
-0.04617
[-2.94695]
-0.0054*
-0.00348
[-1.54968]
-0.08221***
-0.02776
[-2.96081]
-0.13516***
-0.04924
[-2.74500]
0.000782
-0.00196
[ 0.39895]
-0.16395***
-0.0347
[-4.72445]
-0.10082**
-0.0509
[-1.98075]
-0.23211***
-0.05937
[-3.90935]
-0.19155***
-0.05261
[-3.64089]
-0.15597***
-0.03966
[-3.93306]
-0.20918***
-0.07712
[-2.71235]
-0.128*
-0.06737
[-1.89986]
-0.2953***
-0.07751
[-3.80983]
-0.07547***
-0.02894
[-2.60796]
β_ca
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
β_log(RER)
0.080371***
-0.01768
[ 4.54541]
1.40817**
-0.55394
[ 2.54211]
0.061217**
-0.027
[ 2.26715]
0.093885**
-0.03748
[ 2.50483]
-4.46E-01***
-0.17145
[-2.60161]
0.090283***
-0.01604
[ 5.63032]
0.021827
-0.04741
[ 0.46035]
0.006394
-0.03857
[ 0.16578]
0.110034***
-0.02668
[ 4.12421]
0.119155***
-0.01832
[ 6.50290]
0.004418
-0.00759
[ 0.58196]
0.351295***
-0.10412
[ 3.37410]
0.188198***
-0.05434
[ 3.46311]
0.412068***
-0.0987
[ 4.17507]
30
In Table 6 column RER, given the indirect definition used for the RER, positive signs correspond to negative
coefficients for the RER. Shaded areas represent coefficients which have the expected sign.
Page 33 of 45
mex
per
phl
pol
rus
thl
tur
zaf
-0.03694***
-0.01423
[-2.59578]
-0.1061***
-0.03577
[-2.96635]
-0.08823**
-0.04245
[-2.07853]
-0.141***
-0.03813
[-3.69769]
0.009315
-0.01097
[ 0.84902]
-0.09502**
-0.04258
[-2.23120]
-0.06378**
-0.02305
[-2.76761]
-0.11733**
-0.04859
[-2.41479]
1
1
1
1
1
1
1
1
-0.0907
-0.06847
[-1.32468]
0.009982
-0.03892
[ 0.25648]
0.263906***
-0.08568
[ 3.07998]
0.003651
-0.00497
[ 0.73432]
-0.76808***
-0.20494
[-3.74775]
0.252644***
-0.09244
[ 2.73295]
0.052112**
-0.02415
[ 2.15811]
-0.05629
-0.05595
[-1.00602]
Table 9: Cointegrating Vectors (GDP, GDP*, CA, RER) 31
ARG
BOL
BRA
CHL
CHN
COL
α_ca
-0.05973
-0.04786
[-1.24812]
0.004615*
-0.00277
[ 1.66350]
0.011257
-0.00808
[ 1.39242]
-0.08197***
-0.02313
[-3.54292]
-0.00284*
-0.00184
[-1.54592]
-0.16534***
-0.03265
β_log(RER)
0.118565***
-0.00944
[ 12.5583]
-3.47959***
-0.51902
[-6.70420]
-0.05048
-0.05966
[-0.84609]
0.324874***
-0.05335
[ 6.08898]
0.031873
-0.10123
[ 0.31487]
0.13589***
-0.01853
β_GDP
0.004501***
-0.0007
[ 6.44475]
-0.43812***
-0.07005
[-6.25425]
-0.03833***
-0.00688
[-5.57383]
0.021174***
-0.00333
[ 6.35164]
0.010044
-0.01733
[ 0.57973]
0.004343***
-0.00126
β_GDP*
0.027921***
-0.00324
[ 8.61392]
0.384645***
-0.10032
[ 3.83404]
0.007973
-0.01496
[ 0.53281]
-0.01262
-0.00982
[-1.28453]
0.159871***
-0.03566
[ 4.48314]
-0.00022
-0.00369
31
For Column RER, GDP, and GDP*, given the indirect definition used for the RER, positive signs correspond to
negative coefficients for the RER. Shaded areas represent coefficients which have the expected sign.
Page 34 of 45
CZE
ECU
Hun
IDN
IND
ISR
KOR
PER
MEX
MAL
RUS
Pol
PHL
TUR
ZAF
[-5.06386]
-0.38782***
-0.09215
[-4.20849]
-0.27374***
-0.06302
[-4.34370]
0.002332
-0.00832
[ 0.28040]
-0.12751***
-0.03692
[-3.45327]
-0.14473***
-0.0442
[-3.27454]
0.005181
-0.00699
[ 0.74108]
-0.11268***
-0.05707
[-1.97427]
0.005553
-0.00568
[ 0.97842]
-0.00381*
-0.00207
[-1.84200]
-0.0553***
-0.01837
[-3.00999]
-0.15815
-0.15009
[-1.05367]
0.000571
-0.00132
[ 0.43446]
-0.05896
-0.06084
[-0.96907]
-0.00122
-0.00266
[-0.46001]
-0.23509***
-0.04399
[-5.34386]
[ 7.33325]
-0.14227***
-0.03277
[-4.34146]
0.003393
-0.03351
[ 0.10126]
-0.18634
-0.17635
[-1.05664]
0.075465***
-0.01745
[ 4.32587]
0.012647*
-0.00767
[ 1.64837]
1.398577
-0.73304
[ 1.90791]
0.079948
-0.06269
[ 1.27522]
0.31372**
-0.15102
[ 2.07739]
0.633948**
-0.3104
[ 2.04234]
0.345256***
-0.07646
[ 4.51579]
0.14093***
-0.02747
[ 5.13111]
0.361692***
-0.11015
[ 3.28357]
-0.00594***
-0.00154
[-3.86653]
0.239051**
-0.11219
[ 2.13081]
0.00705
-0.02714
[ 0.25975]
[ 3.45463]
0.002373*
-0.00149
[ 1.59733]
0.00479
-0.00475
[ 1.00926]
0.016465
-0.01188
[ 1.38609]
0.006866***
-0.00145
[ 4.75101]
0.009922***
-0.00243
[ 4.07996]
0.084679
-0.01159***
[ 7.30801]
0.010693***
-0.00217
[ 4.92926]
-0.0102
-0.00815
[-1.25189]
0.098975***
-0.016
[ 6.18426]
0.038091***
-0.00585
[ 6.51268]
-0.0109***
-0.00157
[-6.94274]
-0.04053*
-0.0252
[-1.60836]
-0.00594***
-0.00154
[-3.86653]
-0.01768***
-0.00325
[-5.44339]
0.012143***
-0.00263
[ 4.62329]
[-0.05883]
-0.03072***
-0.00511
[-6.00841]
0.019591**
-0.00985
[ 1.98828]
-0.16866***
-0.0334
[-5.04985]
-0.00406
-0.00546
[-0.74396]
0.008732**
-0.00353
[ 2.47355]
-0.0858
-0.06474
[-1.32531]
-0.03142***
-0.00636
[-4.94160]
0.198875***
-0.04007
[ 4.96280]
0.117877***
-0.04364
[ 2.70108]
-0.00019
-0.01491
[-0.01276]
-0.01381**
-0.00707
[-1.95393]
-0.62862***
-0.12568
[-5.00187]
-0.00887**
-0.00444
[-1.99659]
0.075712***
-0.0249
[ 3.04105]
-0.03005***
-0.0047
[-6.39049]
Page 35 of 45
4.2 Charts
Chart 1: CA and Real effective Exchange Rate
140
800
100
.08
60
400 .04
20 .12
0
.04
.00
-.04
-.08
1970
1980
1990
CA_ARG_SA
.10
.05
.00
-.05
-.10
-.15
1970
1980
1990
CA_CHL_SA
.2
.1
.0
-.1
-.2
1970
REER2_ARG
240
200
2000
1980
1990
CA_BOL_SA
160
120 .12
80 .08
.04
.00
-.04
-.08
1970
REER2_CHL
200
160
120
80 .04
40 .02
2000
REER2_BOL
300
250
200
150
100 .08
50 .04
1990
1990
CA_CHN_SA
2000
REER2_CHN
125
1980
.00
1990
CA_IDN_SA
-.04
1970
2000
REER2_IDN
1990
2000
140
1980
REER2_IND
120
100
80
.04
.02
60 .12
.04
.00
-.04
-.02
1970
1980
1990
CA_JAP_SA
-.12
1970
2000
REER2_JPN
1980
1990
CA_KOR_SA
120
80
.050
.000
-.050
-.100
1970
1980
1990
CA_PER_SA
2000
.08
40 .04
.00
-.04
-.08
-.12
1970
REER2_PER
2000
1980
1990
CA_PHL_SA
REER2_PHI
1980
1990
CA_THA_SA
2000
REER2_THL
1980
1990
2000
REER2_CAN
160
120
1990
2000
REER2_COL
120
110
100
90 .04
80 .00
70
-.04
80
40
1980
1990
CA_CZE_SA
2000
REER2_CZE
140
120
100
80
60
40
-.08
1980
1990
REER2_ISR
-.12
1970
2000
1980
1990
2000
1980
1990
CA_MAL_SA
1980
1990
CA_TUR_SA2
2000
CA_ITA_SA
120
100
80
60
40
-.08
1970
2000
REER2_MAL
1,200
1980
1990
CA_MEX_SA
2000
REER2_MEX
400
300
200
400
0
100
.20
0
.10
.00
-.12
1970
1980
1990
2000
-.10
1970
1980
1990
2000
CA_POL_SA
180
.02
60 .00
-.02
-.04
-.06
-.08
1970
REER2_TUR
REER2_ITA
180
160
140
120 .08
100 .04
80
.00
800
100
-.08
1970
CA_ISR_SA
-.04
140
.04
-.04
-.05
-.15
1970
140
120
100
80 .3
60 .2
.1
.0
-.1
-.2
1970
2000
.00
.05
80
CA_CAN_SA
100 .08
60 .04
.00
-.04
-.08
-.12
1970
-.04
120
80
1980
CA_COL_SA
105 .04
.02
95 .00
-.02
-.04
-.06
1970
REER2_KOR
180
160
140
120
100
80 .04
160
.15
REER2_BRA
100
REER2_FRA
CA_GBR_SA
REER2_GBR
CA_HUN_SA
REER2_HUN
500
.2
.04
400
.02
.1
300
.00
.0
200
-.02
-.1 130
100160
140
-.04
0
-.2 120
120
110
100
100
80
90
60
80
1990
2000
1970
1980
1990
2000
1970
1980
1990
2000
CA_IND_SA
.06
120
.04
80 .02
40 .00
-.02
-.04
-.06
1970
140
-.08
1970
2000
140
120
CA_FRA_SA
-.02
1980
2000
160
160
180
115
-.04
1970
REER2_ECU
300
250
200
150
100 .04
50 .02
1990
-.04
1980
.00
1980
1980
CA_BRA_SA
.00
-.02
CA_ECU_SA
.08
.04
.00
-.04
-.08
-.12
1970
-.12
1970
2000
.02
.00
-.02
-.04
-.06
1970
200
1980
REER2_POL
CA_RUS_SA
REER2_RUS
120
280
110
240
100
200
90 .15
160
80 .10
120
70 .05
80
.00
-.05
-.10
-.15
1990
2000
1970
1980
1990
2000
CA_USA_SA
Page 36 of 45
REER2_USA
CA_ZAF_SA
REER2_ZAF
Chart 2
Standard deviation of CA and REER compared
120
Standard deviation of REER
100
RUS
BOL
80
60
CHN
IDN
40
BRA
20
CHL
COL
TUR
ECU
ARG
MAL
THL
JAP
GBR
USA
FRA
0
0
MEX
KOR
ISR
DEU
0.02
0.04
0.06
0.08
0.1
Standard deviation of CA
Chart 3
Real and Nominal Exchange Rate Volatility
(Standard Deviation, 1994-2008)
20
18
CZE
16
JAP
REER
14
HUN
ZAF
POL
12
ISR
10
CHN
CAN
CHL
USA
GBR
8
6
DEU
4
FRA
PER
ITA
PHL
THL
MAL
KOR
IND
2
0
0
5
10
15
20
NEER
Page 37 of 45
25
30
35
40
Source: BIS
-0.05
-0.06
-0.07
Average
Median
-0.02
-0.04
Page 38 of 45
t
t+12
t+10
0.06
t+8
t+6
t+4
-0.04
t+2
-0.03
t-2
-0.02
t-4
Current Account
t-6
0
t-8
Current Accout Deficit Reversals
t-10
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
-0.6
Turkey
Venezuela
Hungary
Russia
India
Poland
Mexico
Germany
France
Brazil
Japan
Indonesia
Peru
Philippines
Israel
China
South Africa
United Kingdom
Italy
Korea
Thailand
Chile
Argentina
United States
Czech Republic
Canada
Malaysia
-0.4
t-10
-0.2
t-12
-0.01
t-12
Chart 4
Correlation between the NEER and the REER
1.2
1
0.8
0.6
0.4
0.2
0
-0.8
Chart 5
CA Surplus reversals
0.08
Current Account
Average
Median
0.04
0.02
0
REER
REER
130
120
120
110
110
100
90
Average
Median
Growth
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-12
t-8
80
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
t-12
90
t-10
100
Average
Median
GDP
7
8
6
7
5
6
5
4
4
3
3
2
Average
Median
2
Average
Median
1
1
0
Chart 6
Total depreciation versus size of CA adjustment
Size of depreciation
-100.00
-90.00
-80.00
-70.00
-60.00
-50.00
-40.00
-30.00
-20.00
-10.00
0.00
5.00
10.00
15.00
0.00
20.00
25.00
Size of CA adjustment
Page 39 of 45
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
t-12
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
t-12
0
Diff in GDP growth pre and post criris
Chart 7
Change in GDP growth versus size of total REER
adjustment
-15
-10
-5
0
-20
-40
-60
-80
-100
0
5
10
15
Size of REER adjustment
Chart 8 : CA deficit reversals : Comparison of crisis vs. Non-crisis periods
Crises periods
Non-crisis periods
Page 40 of 45
Average
Median
t+12
t+8
t+10
t+6
t+4
t
t+2
t-2
t-4
t-8
t-10
t-12
t+12
t+10
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
t-12
t+8
Average
Median
0.02
0.01
0
-0.01
-0.02
-0.03
-0.04
-0.05
-0.06
-0.07
-0.08
t-6
Current Account
Current Account
0.02
0.01
0
-0.01
-0.02
-0.03
-0.04
-0.05
-0.06
-0.07
-0.08
REER
REER
150
150
140
140
130
130
120
120
Average
Median
110
110
Average
Median
100
100
90
t+12
t+10
t+8
t+6
t+4
t
t+2
t-2
t-4
t-6
t-8
t-10
t-12
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
t-12
90
Growth
Growth
7
7
6
6
5
5
4
4
3
3
2
2
Average
Median
1
Average
Median
1
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
t-12
t-12
0
0
Chart 9 CA deficit reversals: Comparison of Fixers vs. Floaters
Fixers
Floaters
Current Account
Current Account
-0.02
-0.02
-0.03
-0.03
-0.06
-0.07
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
-0.04
-0.04
-0.05
t-8
-0.01
t-10
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
-0.01
t-12
0
t-12
0
-0.05
Average
Median
-0.06
-0.07
Page 41 of 45
Average
Median
REER
REER
160
160
Average
Median
150
140
Average
Median
150
140
130
130
120
120
110
110
100
100
90
90
10
8
8
6
6
4
4
2
2
t+12
t+10
t+8
t+6
t+4
t
Average
Median
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
t-12
0
t+12
t+10
Average
Median
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
0
t-12
t+2
Growth
Growth
10
-2
t-2
t-4
t-6
t-8
t-10
t+12
t+10
t+8
t+6
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
t-12
t-12
80
80
Chart 10 CA surplus reversals : Fixers versus floaters32
Fixers
Floaters
Current Account
Current Account
0.1
-0.04
-0.04
32
Note that the sample size for this table is very small (4 fixed ER regimes and 10 floating ER regimes), and
drawing conclusions should be treated with respective caution.
Page 42 of 45
t+12
t+10
Average
Median
t+8
t+6
t+4
t
t+2
t-2
-0.02
t-4
t+12
t+10
t+8
t+6
t+4
t
t+2
-0.02
t-2
0
t-4
0
t-6
0.02
t-8
0.02
t-10
0.04
t-12
0.04
t-6
0.06
t-8
0.06
0.08
t-10
Average
Median
0.08
t-12
0.1
6
6
0
4
2
Page 43 of 45
t+12
8
t+10
8
t+8
10
t+12
t+10
t+8
t+6
t+4
t+2
t
140
t+6
10
t-2
150
t+4
12
t+2
GDP
t
12
t-4
REER
t-2
80
t-6
90
80
t-4
100
90
t-8
110
100
t-6
120
110
t-10
130
120
t-8
Average
Median
t-12
130
t-10
t+12
t+10
t+8
t+6
Average
Median
t-12
t+12
2
t+10
4
t+8
t+4
140
t+6
t+2
t
t-2
t-4
t-6
t-8
t-10
t-12
150
t+4
t+2
t
t-2
t-4
t-6
t-8
t-10
t-12
160
160
REER
Average
Median
GDP
Average
Median
0
Chart 11
Long run cointegration relation REER and CA
1.0000
β_log(reer)
0.8000
0.6000
0.4000
0.2000
Mal
Isr
Phl
Thl
Kor
Idn
Hun
Chl
Col
Arg
Bra
Tur
Cze
Per
Ecu
Ind
Pol
Zaf
Mex
Chn
-0.2000
Rus
0.0000
-0.4000
-0.6000
Chart 12
Short run adjustment of CA to cointegration relation
0.05
-0.1
-0.15
-0.2
-0.25
α_ca
-0.3
-0.35
Page 44 of 45
Rus
Chn
Mex
Tur
Mal
Bra
Phl
Thl
Cze
Per
Zaf
Chl
Arg
Pol
Idn
Col
Hun
Ind
Ecu
-0.05
Kor
0
Chart 13
Adjustment Speed of Current Account versus LevyYeyati and Sturzenegger (2002) classification
0.4
α_ca
0.3
0.2
0.1
Float
Float
Float
Float
Float
Float
Interim
Interim
Interim
Interim
Interim
Interim
Interim
0
IDN KOR ARG BRA MEX PER ECU RUS ISR COL CHL CAN JPN
Note: Levy-Yeyati and Sturzenegger (2002) classify countries into floaters, intermediate, and fixed
regimes from 1975 to 2000. We average the indicator over the period 1980-2000 to evaluate the
"average" de facto exchange rate regime. Countries are ranked according to this average here.
We only consider statistically significant error-correction terms.
Page 45 of 45