DOI: 10.5817/FAI2017-1-4 No. 1/2017
58
The Connection between the Exchange Rate
and the Balance of Payments Accounts in the Czech
Republic: An Econometric Approach
Tomáš Urbanovský
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová 507/41a, 602 00 Brno, Czech Republic
E-mail: urbanovsky@mail.muni.cz
Abstract: Relationships between the nominal exchange rate, the current
account and the financial account of the balance of payments in the Czech
Republic are investigated in this presented paper. The implemented
cointegration analysis and vector error correction model suggest one pair of
Granger causality. It has been discovered that change in the current account
balance Granger-causes a change in financial account balance. This
relationship has the nature of two-way Granger causality, which means that a
reversed relationship holds as well. Other relationships implying Granger
causality were not found. Error terms were significant only in regressions with
both accounts as dependent variables, which imply that only these variables
return to their long-term equilibria. Because an increase in financial account
surplus leads to a decrease in current account surplus (or deepening the
current account deficit), excessive liberalization of the Czech financial system
can lead to a large capital inflow, jeopardizes current account sustainability
and results in a currency crisis in the Czech economy.
Keywords: exchange rate, balance of payments, cointegration, VECM
JEL codes: C32, C51, F32
Introduction
Currency crises, sometimes known as balance of payments crises, are still a
very real danger to market economies. The experience of currency crises
indicates that a persistent current account deficit serves as a warning of an
impending crisis. The aim of this paper is to find out whether the development
of a nominal exchange rate and financial account affect the development of
the current account in the Czech Republic or not. The proved existence of
these relationships can be very important for the Czech monetary authority
because a proper monetary policy can affect the development of the financial
account and (or) nominal exchange rate, which can prevent an undesirable
worsening of the current account (which can result in a currency crisis in the
Czech Republic).
Because there are three variables, it is possible to find three potential
relationships. The relationship between current and financial account proceeds
from the balance of payments identity. According to the balance of payments
identity, a country’s current and financial account have to be balanced ex post,
meaning that trade deficits (surpluses) will have to be accompanied by net
capital inflows (outflows) of the same magnitude. Therefore, a negative
correlation between the current and financial account must exist. However, the
59
direction of causality is not clear at first sight and usually depends on the
sample of countries under study (whether countries are developed or
developing), as well as on the length of the period.
According to Oeking and Zwick (2015), the current account generally Grangercauses
the financial account in OECD countries. However, for short-term flows,
the direction changes over the business cycle: financial account components
finance the current account during economic downturns while inducing its
changes during upturns. In Yan (2005), Yan and Yang (2008) and Guerin
(2004) Granger causality tests were implemented to find out that the financial
account is responsible for the current account in developing countries - instead
of financing the current account, the financial account thrusts the current
account into an imbalance. Ersoy (2011) used Granger causality analysis
under a VAR framework to reach the same conclusion for the Turkish economy
- unidirectional causality runs from the financial account to the current
account. Mastroyiannis (2012) examined the Portuguese economy and the
implemented cointegration analysis suggests the existence of a long run
relationship between foreign capital inflows and the current account position
that is based upon a unidirectional causal long-run relationship, running from
foreign capital inflows to current account position. He found a bidirectional
relationship between the two variables in the short-run.
Nevertheless, Yan (2005) discovered a reversed relationship in developed
countries - the financial account serves to finance a current account
imbalance. Lau and Fu (2011) also observed that the current account Grangercauses
the financial account suggesting that a current account can be used as
the control policy variable for the flows of capital in the Asian countries of
Indonesia, Korea, the Philippines and Thailand. The same conclusion can be
found in Tang (2014) - this time for the US economy. And finally, Turan
(2015) investigated the relationship between the current and financial account
in several CEE countries, but results differ across the selected sample of
countries.
Forogue and Veloce (1990) empirically proved the existence of bidirectional
causality between the financial and the current accounts of Canada. Kim and
Kim (2010) proved the same bidirectional causality for Korea. Fry et al. (1995)
find that some developing countries have unidirectional, some have
bidirectional and some have no causality between the financial and the current
accounts.
Current account and nominal exchange rate are connected through
international trade. Increased (decreased) exports will increase (decrease) the
demand for the domestic currency, and, subsequently, cause an appreciation
(depreciation) of the domestic currency. A second factor is imports
development. Increased (decreased) imports will increase (decrease) the
supply of the domestic currency, and, subsequently, cause a depreciation
(appreciation) of the domestic currency. But again, the direction of causality is
not clear at first sight – it is possible that the causality flows from the
exchange rate to the components of the current account. Studies on the
relationship between a current account and the nominal exchange rate are
60
based on theoretical macroeconomic models elaborated mostly in the 1970s
and 1980s. Dornbusch and Fischer (1980) suggest the existence of a causal
relationship. In particular, that a current account is an important element in
exchange rate determination. On the other hand, Martin (2016) used a panel
of 180 countries over the 1960–2007 period and found evidence for a reversed
relationship, which holds especially in non-industrial countries – flexible
exchange rate arrangements deliver a faster current account adjustment.
According to Larrain (2003), the relationship has the nature of a two-way
causality. In particular, that exchange rate determines the current account,
and the current account, in turn, determines the exchange rate.
A financial account and the nominal exchange rate are connected through
capital flows. An inflow of foreign capital will increase the demand for domestic
currency, and, subsequently, cause an appreciation of the domestic currency.
An outflow of foreign capital will increase the supply of domestic currency, and,
subsequently, cause a depreciation of the domestic currency. Again, the
direction of causality can differ across the economies under study. Regarding
the relationship between a financial account and the nominal exchange rate,
Siourounis (2003) implemented an unrestricted VAR and found causality
flowing from the financial account to the exchange rate in the UK, Germany,
Switzerland and Japan. Gyntelberg et al. (2015) reached the same conclusion
for the economy of Thailand. On the other hand, Kandil (2009) proved that
fluctuations in the exchange rate are an important determinant of the financial
balance in developing countries, but fluctuations in capital flows appear, in
general, to be random in many developing and industrial countries, with
limited evidence regarding the systematic correlation with exchange rate
fluctuations.
Because of the reasons mentioned in the first paragraph of this section, the
aim of this paper is to examine the nexus among the nominal exchange rate,
and the current and financial account of balance of payments in the case of
the Czech Republic. The rest of the paper is organized as follows. Section 2
covers a description of the data and relevant methodology. Section 3 presents
the results of the analysis. Last section contains a summary and conclusions.
1 Methodology and Data
The presented analysis works with the above-mentioned macroeconomic
variables: the nominal exchange rate, the current account and the financial
account of the balance of payments. Data on each variable was acquired via
the time series database ARAD administered by the Czech National Bank and
processed in the econometric software Gretl. ARAD only contains data since
1995. Therefore, seasonally adjusted quarterly data on these variables for the
Czech Republic from 1995Q1 to 2015Q4 is used, which means that 84
observations for each variable were collected from this period. Table 1
contains a short description of the variables and their abbreviations used in
the analysis.
61
Table 1 Variables used in the analysis
abbreviation
of variable
variable characteristic
cur current account balance expressed in millions of CZK
fin financial account balance expressed in millions of CZK
rate nominal exchange rate expressed as CZK/USD
Source: Author's work
It is possible that the exchange rate CZK/EUR would be more appropriate
because Eurozone countries are the most important trade partners of the
Czech Republic. Instead, CZK/USD was used and there are two main reasons
for this decision:
 The euro came into existence on January 1, 1999, therefore, it is only
possible to get 68 quarterly observations, which means a relatively short
time series. This fact can lead to a potential robustness worsening of the
empirical analysis.
 The current instability in the Eurozone (Brexit, the Greek crisis, the
migration crisis, etc.) results in a high volatility of the euro. This volatility
is independent of the situation of the Czech economy. With the use of
CZK/EUR in the analysis, there would be the risk of an occurrence of no
significant relationship between CZK/EUR and the macroeconomic
indicators of the Czech Republic.
A typical characteristic for the time series is a correlation across the
observations. Therefore, an appropriate number of lags for each variable needs
to be determined to assess which data is independent. This step is crucial
because, as soon as an autoregressive model of the appropriate order for each
variable is found, it will be possible to test the (non)stationarity of variables. A
sequential testing procedure is the most convenient way to determine the lag
length for each variable. The chosen number of lags will be included in order to
assess the intensity of the influence of previous values on the current value.
Then lag lengths would be sequentially dropped if the relevant coefficients turn
out to be statistically insignificant.
Because the model works with quarterly data, it is reasonable to suspect that
the value of the variable from the same period a year before can help explain
the value in the current period (i.e. the value in the second quarter of 2012
can explain the value in the second quarter of 2013, etc.). For this reason, the
highest number of lags is set at four. After omitting insignificant lags, this final
regression will be known as the autoregressive model of order p (AR(p) model)
and because three variables are considered in the analysis, there will be three
equations. The AR(p) model can be expressed more formally as:
62
= + ∑ + (1)
where represents the corresponding variable.
A crucial characteristic of a non-stationary time series is the presence of a unit
root. The presence of a unit root is demonstrated by the coefficient ϕ1 equal to
the unity in equation (1). However, for testing unit root behaviour, it is
convenient to subtract from both sides of the equation (1). Formally:
= + + ∑ + (2)
where ρ = ϕ–1. ρ = 0 implies that the original time series, in the form of
AR(p), contains the unit root and is non-stationary. A test performed by means
of equation (2) is called an augmented Dickey-Fuller test, invented in Dickey
and Fuller (1979) - from now on it will be abbreviated as the ADF test.
Testing for the presence of unit roots in variables under study follows the
procedure based on ADF tests. This procedure was originally developed by
Dolado, Jenkinson and Sosvilla-Rivero (1990). The procedure used in this
paper is a modification of this procedure done by Enders (2010). Because the
actual data-generating process is not known, it seems reasonable to start
testing the hypothesis ρ = 0 using the general model, which also includes a
time trend component. Formally:
= + + + ∑ + (3)
The ADF test is very popular. However, it is not sufficient to be interested only
in (testing) the value of coefficient ρ, because results of ADF tests can be
influenced by the statistical significance of a trend and (or) constant. For these
reasons, joint hypotheses concerning α, δ and ρ need to be tested. The whole
testing procedure proceeds from equation (3) and has a complex multistage,
in particular, see figure 1.
Please note that the original procedure covers all possible options an
econometrician can encounter. The presented procedure, on the other hand, is
simplified and is shadowing the outcomes of the model presented in the
following section of paper (there is no need to present the whole procedure
because all variables under study follow the same pattern, and some options
of the original procedure are redundant for the purpose of this paper). Also
note that in equations (3), (4) and (5), lag lengths discovered earlier in AR(p)
processes are respected.
63
Figure 1 The unit root testing procedure
∆xt = α + δt + ρxt-1 + Σγi∆xt-i + εt
∆xt = α + ρxt-1 + Σγi∆xt-i + εt (4)
∆xt = ρxt-1 + Σγi∆xt-i + εt (5)
Yes: Test for the presence of the
trend
is ρ = 0 ?
is δ = ρ = 0 ?
estimate
estimate
Conclude (xt) has
a unit root
is ρ = 0 ?
Yes
estimate
Yes
Yes: Test for the presence of the
constant
Yes
is α = ρ = 0 ?
is ρ = 0 using
normal distribution?
No
Conclude (xt) has
a unit root
Yes
is ρ = 0 ?
Source: Author’s own elaboration based on Dolado, Jenkinson and Sosvilla-Rivero
(1990) and Enders (2010)
Model specification
Previous studies showed that it is likely to find these variables to be nonstationary.
Non-stationarity of all selected variables makes it possible to test
the sample for the existence of cointegrating relationships among variables.
Before proceeding to Johansen’s cointegration test, developed in Johansen and
Juselius (1990), it is necessary to find the appropriate number of lags for each
variable. The highest number of lags is set at a level of four (for the same
reason as discussed in the part of the paper dealing with the specification of
AR processes). The decision of the most appropriate number of lags is based
on information criteria – Akaike criterion (AIC) developed in Akaike (1974),
Schwarz Bayesian criterion (BIC) developed in Schwarz (1978) and HannahQuinn
criterion (HQC) developed in Hannah and Quinn (1979).
After the lag length determination, it is possible to perform Johansen’s
cointegration test to find out how many cointegrating relationships are present
among the variables, i.e. to specify the cointegration rank. It was decided to
64
use both a trace test and maximum eigenvalue test in order to find out
whether the same conclusion would be reached or not.
Once the cointegration rank is determined, a short-term analysis in the form of
a vector error correction model (VECM) can be performed. This particular
model is convenient because it allows the identification of the patterns of
Granger causality between variables. Furthermore, the inclusion of an error
correction term (ECT) is crucial to determine whether variables displaying the
previous period’s deviation from long-run equilibrium are drawn back to their
long-run equilibria or not. Formally, VECM can be written as:
∆ = + ∑ ∆ + ∑ ∆ + ∑ ∆ + ∑ + (6)
To be more precise, this is only one of a total number of three equations,
which together form VECM. These three equations differ in the dependent
variable. Each equation regresses a dependent variable on the selected
number of lags of all the variables in the VECM (the number of lags p is
determined by the information criteria). Only one equation is stated to save
space. Note also that the number of ECTs depends on the cointegration rank
(r) – the number of cointegrating relationships is equal to the number of ECTs.
The statistical significance of the corresponding coefficient implies Granger
causality, meaning that a variable with this coefficient Granger-causes a
dependent variable.
2 Results and Discussion
Simple OLS regressions were used to determine the order of AR(p) process for
each variable under study in order to find out a potential correlation between
the consecutive values of each variable. The results of the performed OLS
regressions of variables under study on their lags are stated in Table 2 (only
coefficient estimates and p-values are included in the table in order to save
space). Please note that the expression X in the first column always represents
the lags of a corresponding dependent variable.
Regression results for variable cur and fin correspond with the assumption
presented in the previous section of the paper. In particular that the value of a
variable from the same period a year before can help explain the value in the
current period. In the case of a variable rate, statistically insignificant lags
were sequentially dropped from the regression in order to receive more
accurate estimations of significant coefficients – these dropped lags are
illustrated by blank spaces. It is obvious that the current value of the variable
rate depends only on the instantly preceding value.
65
Table 2 OLS regressions of variables under study on their lags
coef. p-value coef. p-value coef. p-value
const. -7601.44 0.1499 868.712 0.8373 0.8089 0.2764
Xt-1 0.0012 0.9899 0.0926 0.3081 0.9680 2.36e-050 ***
Xt-2 -0.1131 0.2256 0.0498 0.5842
Xt-3 0.0548 0.5563 0.06 0.4894
Xt-4 0.6624 7.27e-09 *** 0.7149 6.00e-010 ***
R2
adjusted R2
P-value (F)
explanatory
variables
curt fint
0.412094 0.420036
ratet
0.936856
3.68e-08 2.25e-08 2.36e-50
dependent variable (Xt)
0.380739 0.389094 0.936077
Note: Significance at ***1%
Source: Author’s own elaboration based on Gretl output and on data acquired via ARAD
Based on the results, it is possible to see that the best way to describe the
behaviour of variable cur and fin is by means of AR(4) processes and a
variable rate by means of an AR(1) process. Formally:
= + ∑ + = + ∑ + (8)
= + + (9)
After the specification of AR processes, it is possible to construct equations for
ADF tests. This is achieved by the subtraction of the first lag of the dependent
variable in each equation:
= + + ∑ + (10)
= + + ∑ + (11)
= + + (12)
The next step is testing for the presence of unit roots. Testing follows the
scheme mentioned in the previous section. The results of this procedure for
each variable are stated in Appendix A (only characteristics crucial for deciding
the existence of unit roots are stated). From t-statistics, it is obvious that
coefficients ρ are statistically insignificant in the cases of all variables, i.e. null
hypotheses that they are equal to zero cannot be rejected. Therefore, the
conclusion is that all original time series are first-order integrated or I(1),
which means every variable contains a unit root and displays non-stationary
behaviour.
66
All variables are non-stationary, therefore, testing for the existence of
cointegration is desirable. The first step is to find an appropriate number of
lags with the use of information criteria. The best option appears to be an
option with one lag according to BIC and HQC (although AIC suggests the
inclusion of four lags).
Table 3 Lag length determination
lags 1 lags 2 lags 3 lags 4
AIC 50.290260 50.268223 50.440386 50.027656
BIC 50.558238 50.804179 51.244320 51.099568
HQC 50.39770 50.483103 50.762706 50.457416
Source: Author’s own elaboration based on Gretl output and on data acquired via ARAD
Now it is time to proceed to Johansen’s cointegration test to find out how
many cointegrating relationships are present among the variables, i.e. to
specify the cointegration rank. The results are summarized in Table 4.
According to the trace test, the null hypothesis of existence of two
cointegrating relationships cannot be rejected. The same outcome is received
with the usage of a maximum eigenvalue test. Therefore, both tests suggest
that the subsequent short-term analysis should have the form of a VECM with
a cointegration rank 2.
Table 4 Rank determination (Johansen cointegration test)
Null
Hypothesis
Alternative
hypothesis Eigenvalue Test statistic p-value
r = 0 r > 0 0.52979 109.99 0.0000
r ≤ 1 r > 1 0.42579 47.363 0.0000
r ≤ 2 r > 2 0.015748 1.3175 0.251
r = 0 r = 1 0.52979 62.63 0.0000
r = 1 r = 2 0.42579 46.045 0.0000
r = 2 r = 3 0.015748 1.3175 0.251
Trace test
Maximum eigenvalue test
Source: Author’s own elaboration based on Gretl output and on data acquired via ARAD
The outputs of the estimated VECM with a cointegration rank 2 are stated in
Table 5. Note that the lag length established earlier is respected in the VECM
analysis. Therefore, each variable is regressed on one lag of all variables under
study plus two error correction terms (because two cointegrating relationships
are present). Again, only coefficient estimates and p-values are included in the
table in order to save space.
67
Table 5 A VECM with a cointegration rank 2
coef. p-value coef. p-value coef. p-value
const. -22319.0 0.0002 *** -675.78 0.9202 0.0668 0.8498
Δcurt-1 0.6744 0.0003 *** 0.6049 0.0051 *** −9.09952e-06 0.4086
Δfint-1 -0.4469 0.0059 *** -0.5268 0.0056 *** 6.43684e-06 0.5070
Δratet-1 1011.49 0.6010 2519.70 0.2673 -0.0300 0.7998
ECTt-1 -1.5895 0.0000 *** -0.4955 0.0959 * 4.11315e-06 0.7896
ECTt-2 -0.3614 0.0088 * -0.4619 0.0634 * −3.48784e-06 0.7862
R
2
DW stat.
Δfint
explanatory
variables
dependent variable
0.586298
2.034006
0.518505
2.034319
0.013576
2.006379
ΔratetΔcurt
Note: Significance at *10%, ***1%
Source: Author’s own elaboration based on Gretl output and on data acquired via ARAD
The first interesting finding is that two of three regressions display a relatively
high value of determination coefficient (R2
) – in the case of dependent variable
Δcurt, almost 59 %, which means that the given variables explain at 58.62 %
the variability of variable Δcurt. Only the equation with dependent variable
Δratet has a low R2
(1.35 %) due to the statistical insignificance of all
explanatory variables.
The last row represents the values of Durbin-Watson statistics. All values are
relatively close to value 2, which indicates the desirable property of
regressions - no autocorrelation of residuals.
Looking at the regressions themselves reveals several facts. First of all, the
lags of the dependent variables are statistically significant in regressions with
Δcurt and Δfint as dependent variables. It means that current changes in
variables Δcurt and Δfint can be explained by the previous changes of these
variables. This is clearly not the case with dependent variable Δratet because
the previous value of Δratet has no explanatory power.
Secondly, only one pattern of Granger causality is obvious from the table. In
the equation with Δcurt as the dependent variable, it is clear that past financial
account balance change has an explanatory power for the current account
balance change – in other words, past financial account balance change
Granger-causes a current account balance change. And from looking at a
regression with Δfint as the dependent variable, it is obvious that a reversed
relationship exists - past current account balance change Granger-causes a
financial account balance change. Because causality flows in both directions,
two-way Granger causality exists between the current account and the
financial account of the balance of payment. And because there is a negative
sign in both cases (-0.4469 and -0.6049), a positive change of the explanatory
variable is associated with a negative change of the dependent variable and
vice versa. Specifically, an increase (decrease) in the financial account balance
is linked to a worsening (improvement) of the current account balance, and an
increase (decrease) in the current account balance is linked to a worsening
(improvement) of the financial account balance. This is in accordance with the
balance of payments identity mentioned in the first section of the paper.
68
Finally, error correction terms are significant in two regressions, which
correspond with the two previously discovered cointegrating relationships.
Corresponding coefficients also have a negative sign, which is crucial for VECM.
This necessity is demonstrated on the variable cur. If the error term in the
previous period is positive (negative), the previous value of the variable cur is
too high (low) to be in equilibrium. And because this positive (negative) error
term will be multiplied by a negative coefficient, the product will be negative
(positive) and the variable cur will start falling (rising) in the next period and
the error will be corrected. The same holds for variable fin. This means that
these two variables are drawn back to their equilibria. In other words, past
equilibrium error is corrected in the model only in the cases of these two
variables.
This reasoning does not hold in the case of a variable rate because error
correction terms coefficients are not statistically significant. And what's more,
exchange rate change not only does not have any explanatory power in
regressions with Δcurt and Δfint as dependent variables, but also Δcurt and
Δfint cannot help explain an exchange rate change. It means that there is no
connection between the exchange rate and these two accounts of the balance
of payments. Therefore, changes in exchange rate should not affect the
balance of payments development, and vice versa – exchange rate
development should be independent of balance of payments development.
The main contribution of the presented empirical paper is that bidirectional
causal relationship between a current and financial account has been proved in
the case of the Czech Republic. Similar conclusions were reached by Forogue
and Veloce (1990) for the Canadian economy and Kim and Kim (2010) for the
Korean economy. Crucial is the situation when an increase in financial account
surplus leads to a decrease in current account surplus (or deepening the
current account deficit) because, as mentioned in the first section of this
paper, a long-lasting current account deficit can result in a currency crisis. The
final conclusion is that excessive liberalization of the Czech financial system
can lead to a large capital inflow and put current account sustainability into
jeopardy.
These findings from the presented empirical study complement current
research studies on issues related to the interaction between financial account
development and the adjustment of current account imbalance.
Conclusions
The relationships between three macroeconomic variables – the nominal
exchange rate, the current account and the financial account of the balance of
payments - in the Czech Republic were investigated in the presented paper.
After a brief theoretical insight into this issue, data and the methodology used
in subsequent analysis were introduced. Several facts have been found in the
empirical part of the paper. ADF tests, performed after the determination of
the order of AR processes, proved that each variable under study contains a
unit root and, therefore, displays non-stationary behaviour. The subsequently
performed Johansen’s cointegration test suggests that two cointegrating
69
relationships are present among the variables, which means that the resulting
model took the form of a vector error correction model with a cointegration
rank 2. The implemented VECM showed that two-way Granger causality exists
between a current and financial account, in particular: an increase (decrease)
in a current account balance leads to a decrease (increase) in a financial
account, and vice versa. The statistical significance of error terms also suggest
that these two variables return to their long-term equilibria. Because an
increase in financial account surplus leads to a decrease in current account
surplus, the main conclusion of the presented paper is that the excessive
liberalization of the Czech financial system can lead to a large capital inflow,
put current account sustainability into jeopardy and elicit a currency crisis in
the Czech Republic.
Acknowledgements
The support of the Masaryk University internal grant MUNI/A/1039/2016 is
gratefully acknowledged.
References
Akaike, H. (1974). A new look at the statistical model identification. IEEE
Transactions on Automatic Control, 19(6), pp. 716–723.
Dickey, D. A. and Fuller, W. A. (1979). Distribution of estimators for time
series regressions with a unit root. Journal of the American Statistical
Association, 74, pp. 427-431.
Dolado, J., Jenkinson, T. and Sosvilla-Rivero, S. (1990). Cointegration and
Unit Roots. Journal of Economic Surveys, 4(3), pp. 249-273.
Dornbusch, R. and Fischer, S. (1980). Exchange Rates and the Current
Account. The American Economic Review, 70(5), pp. 960-971.
Enders, W. (2010). Applied econometric time series (3rd ed.). Hoboken: Wiley.
Ersoy, I. (2011). The Causal Relationship between the Financial Account and
the Current Account: The Case of Turkey. International Research Journal of
Finance and Economics, 75, pp.187-193.
Faroque, A. and Veloce, W. (1990). Causality and Structure of Canada’s
Balance of Payments. Empirical Economics, 15, pp. 267-283.
Fry, M., Claessens, S., Burridge, P. and Blanchet, M-C. (1995). Foreign Direct
Investment, Other Capital Flows and Current Account Deficit: What causes
what? World Bank Policy Research Working Paper, no.1527.
Guerin, S. S. (2004). The relationship between capital flows and current
account: volatility and causality. ECARES, Université Libre de Bruxelles.
Gyntelberg, J., Loretan, M. and Subhanij, T. (2015). Private information,
capital flows, and exchange rates. Swiss National Bank: Working paper, no.
12.
Hannan, E. J. and Quinn, B. G. (1979). The Determination of the order of an
autoregression. Journal of the Royal Statistical Society, Series B, 41, pp. 190–
195.
70
Johansen, S. and Juselius, K. (1990). Maximum Likelihood Estimation and
Inference on Cointegration - with Applications to the Demand for Money.
Oxford Bulletin of Economics and Statistics, 52, pp. 169-210.
Kandil, M. (2009). Exchange Rate Fluctuations and the Balance of Payments:
Channels of Interaction in Developing and Developed Countries. Journal of
Economic Integration, 24(1), pp. 151-174.
Kim, C.-H. and Kim, D. (2010). Do Capital Inflows cause Current Account
Deficits? Applied Economics Letters, 18(5), pp. 497-500.
Larrain, M. (2003). Central bank intervention, the current account, and
exchange rates. International Advances in Economic Research, 9(3), pp. 196-
205.
Lau, E. and Fu, N. (2011). Financial and current account interrelationship: An
empirical test. Journal of Applied Economic Sciences, 6(1), p. 34-42.
Martin, F. E. (2016). Exchange rate regimes and current account adjustment:
An empirical investigation. Journal of International Money and Finance, 65, pp.
69-93.
Mastroyiannis, A. (2012). Causality Relationships in the Structure of Portugal’s
Balance of International Payments. International Journal of Business and
Social Science, 3(15), pp. 54-61.
Oeking, A. and Zwick, L. (2005). On the Relation between Capital Flows and
the Current Account. Ruhr Economic Papers #565.
Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of
Statistics, 6(2), pp. 461–464.
Siourounis, G. (2003). Capital Flows and Exchange Rates: An Empirical
Analysis. London Business School, IFA Working Paper, No. 400.
Tang, T. C. (2014). Fiscal Deficit, Trade Deficit, and Financial Account Deficit:
Triple Deficits Hypothesis with the U.S. Experience. Monash University
Malaysia: DISCUSSION PAPER, vol. 6.
Turan, T. (2015). Causal relationship between current account and financial
account balance in selected CEE countries. Journal of Economics, 63(9), pp.
959-974.
Yan, H. and Yang, C. (2008). Foreign Capital Inflows and the Current Account
Imbalance: Which Causality Direction? Journal of Economic Integration, 23(2),
pp. 434-461.
Yan, H.-D. (2005). Causal Relationship between the Current Account and
Financial Account. International Advances in Economic Research, 11(2), pp.
149-162.
71
Appendix A: Testing for the presence of a unit root
 variable cur
t-stat. -2.361 F-stat. 3.46522 t-stat. -1.871 F-stat. 1.78962 t-stat. -1.201
crit. value -3.45 crit. value 6.49 crit. value -2.89 crit. value 4.71 crit. value -1.95
H0 not rejected H0 not rejected H0 not rejected H0 not rejected H0 not rejected
time trend is not significant constant is not significant
sequence contains a
unit root
H0: ρ = 0 H0: δ = ρ = 0 H0: ρ = 0 H0: α = ρ = 0 H0: ρ = 0
conclusion conclusion conclusion conclusion conclusion
∆curt = α + δt + ρcurt-1 + … ∆curt = α + ρcurt-1 + … ∆curt = ρcurt-1 + …
step 1 step 2 step 3 step 4 step 5
Source: Author’s own elaboration based on Gretl output and on data acquired via ARAD
 variable fin
t-stat. -1.589 F-stat. 2.88899 t-stat. -0.4679 F-stat. 0.352421 t-stat. -0.819
crit. value -3.45 crit. value 6.49 crit. value -2.89 crit. value 4.71 crit. value -1.95
H0 not rejected H0 not rejected H0 not rejected H0 not rejected H0 not rejected
time trend is not significant constant is not significant
sequence contains a
unit root
H0: ρ = 0 H0: δ = ρ = 0 H0: ρ = 0 H0: α = ρ = 0 H0: ρ = 0
conclusion conclusion conclusion conclusion conclusion
∆fint = α + δt + ρfint-1 + … ∆fint = α + ρfint-1 + … ∆fint = ρfint-1 + …
step 1 step 2 step 3 step 4 step 5
Source: Author’s own elaboration based on Gretl output and on data acquired via ARAD
 variable rate
t-stat. -1.641 F-stat. 1.34745 t-stat. -1.088 F-stat. 0.594544 t-stat. -0.3469
crit. value -3.45 crit. value 6.49 crit. value -2.89 crit. value 4.71 crit. value -1.95
H0 not rejected H0 not rejected H0 not rejected H0 not rejected H0 not rejected
time trend is not significant constant is not significant
sequence contains a
unit root
H0: ρ = 0 H0: δ = ρ = 0 H0: ρ = 0 H0: α = ρ = 0 H0: ρ = 0
conclusion conclusion conclusion conclusion conclusion
∆ratet = α + δt + ρratet-1 + … ∆ratet = α + ρratet-1 + … ∆ratet = ρratet-1 + …
step 1 step 2 step 3 step 4 step 5
Source: Author’s own elaboration based on Gretl output and on data acquired via ARAD