COINTEGRATION AND CAUSALITY AMONG EXCHANGE RATE, EXPORT, AND IMPORT: EMPIRICAL EVIDENCE FROM TURKEY SEKMEN, Fuat* SARIBAS, Hakan Abstract
This paper examines the cointegration and causality among exchange rate, export, and import for Turkey during the period of 1998-2006. The econometrics results show that there is a cointegration between exports and import, but direction of causality is bi- directional between these two variables. The impulse response functions also supports that there is a trade-off between exports and imports; for example, when imports are high, there is smaller exports at that time. This study supports few investigators who find no negative effect of exchange rate volatility on trade volume since it is found that exchange rates cannot determine the variation in exports and imports.
JEL Classifications: F41, C3/C32
Key Words: Export, Import, Exchange Rate, Cointegration, and Impulse Response Function 1. Introduction
This study examines relationship among exchange rate, export, and import for Turkish economy. Previous studies have investigated the effect of exchange rate on trade volume but they have not reached an agreement among themselves. Some economists, such as Ethier (1973), Hooper and Kohlhagen (1978), Kumar and Dhawan (1991), Gagnon (1993), Broll (1994), Caporale and Dorodioon (1994), Wolf (1995), Dell’Ariccia (1998), Rose (2000), and Vergil (2002) illustrate that exchange rate volatility reduces international trade. The main idea behind the intuition of exchange rate volatility decreases trade volume is that exchange rate volatility increases uncertainty, which in turn decreases trade volume.
On the other hand, De Grauwe (1988), and Giovannini (1988), Franke (1991), De Grauwe (1988), Franke (1991), Grobar (1993), McKenzie and Brooks (1997), Dewlin et al. (2001) stated that exchange rate volatility or risk may actually stimulate trade flows since uncertainty is considered as an option held by firms, which increases profitability.
Kroner and Lastrapes (1993), McKenzie et al. (1998), and Aristotelous (2001), in contrast to other studies, find no certain evidence for the relationship between exchange rate volatility and trade volume. Moccero and Winograd (2006) investigate the effect of exchange rate volatility by examining the intra and extra regional exports and they conclude that reducing volatility has a positive impact on exports to Brasil, but a detrimental effect on exports to the rest of the world. This result also supports the view that there is an ambiguity in the role of exchange rate volatility on trade volume.
There are a few studies which examine separately the negative effect of exchange rate volatility on imports, for example, Gotur (1985) and Cushman (1986) investigated the
* Fuat Sekmen, Sakarya University, Department of Economics, Ass. Prof.Dr., Sakarya, Turkey, [email protected]. Hakan Saribas, Zonguldak Karaelmas University, Department of Public Finance, Ass. Prof.Dr., Zonguldak, Turkey.
effect of exchange rate volatility on imports. Most of the studies have examined the effect of exchange rate volatility on exports and on trade volume.
This paper has focused on the association among exchange rates, export, and import rather than the effect of exchange rate.1 This study will also shed light the direction of causality among exchange rate, export, and import. The organization of the paper will be as followed: section 2 represents methodology and data, section 3 shows the results and discussions, and section 4 presents conclusion.
2. Data and methodology
Monthly data for Exports (X) and Imports (M) for 1998-2006, at current prices and exchange rates, million US Dollars, Source: Turkish Treasury. Nominal exchange rate:
The end of each month is taken into account, Purchasing of 1USD against Turkish New Liras. After 2005, New Turkish Liras has been used instead of Turkish Liras. Source:
Turkish Central Bank(2006). Graph 1 shows the evolution of Exports and Imports per inhabitant in Turkey in dollars at current prices, and graph 2 present the evolution of the exchange rate in new liras.
Graph 1. Foreign trade in Turkey Graph 2. Exchange rate of Turkey (current dollars per inhabitant) (new Turkish liras per dollar)
400 600 800 1000 1200 1400 1600
94 95 96 97 98 99 00 01 02 03 04
Imports per inhabitant
Exports per inhabitant
0.0 0.4 0.8 1.2 1.6
96 97 98 99 00 01 02 03 04 05 06
Econometric methodology firstly examines time series selected if they exhibit stationarity or not because these tests are necessary. For this purpose, this paper uses the Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) tests. A methodological note on these tests is included in the Annex.
Secondly Cointegration is analysed with Johansen test and causality with Granges test (see methodological note in the Annex). After being found cointegration among series taken in this study, the paper will examine the relationship among exports, imports, and exchange rate for Turkey using variance decomposition methods. For this purpose,
1 The paper uses nominal exchange rate data. Next study can compare the results of nominal exchange rate and real exchange rate.
monthly data for exports, imports, and nominal exchange rates of Turkey since 1998 until 2006 will be used in this study.
3. Results and Discussions
3.1. Results of the unit root tests: The results of the ADF and the PP tests for stationarity properties of the variables are presented in Table A1. The table A1 in the Annex shows that the τ statistics for all the variables (X, M, and ER) are not significantly negative since they are positive and greater than the critical values at, respectively, 1%, 5%, and 10% levels from both the ADF and the PP tests. Thus, it is not possible to reject the null hypothesis of the presence of the unit root for the variables. However, the results of the first differenced variables indicate that the ADF test statistics for all the variables are significantly negative, therefore, the null hypothesis of unit root can be rejected in all variables at 1%, 5%, and 10% levels. The PP test also shows that after differencing all the variables are stationary, meaning that all the variables are integrated of order I(1).
3.2. Results of ADF Cointegration Tests: In order to find out if the variables are cointegrated, the ADF unit root test was applied on the residuals from the three equations, where M was regressed on X in the first equation, then ER was regressed on X in the second equation, and in the third equation ER was again regressed on M. Table A2 in the Annex presents the results from this analysis. From the table, τ statistics on residuals for M regressed on X is less than the critical value at 10 % level, thereby null unit root hypothesis is rejected for the equation 1. However, the null unit root hypothesis cannot be rejected since τ statistics on residuals are not less than the critical values at 1%, 5 %, and 10 % level. These results suggest that only M is cointegrated with X. This result is meaningful in respect of economics theory because if a country’s trade balance is not good today, this does not mean that this country may not achieve a sustainable growth in the future. For example, if intermediate goods which are crucial for exports are imported and there is a trade balance deficit, this country can succeed export-lead growth in the long run. Thus, in my view, Turkish trade deficit is not so big problem as long as today’s imports can lead to higher exports in the future.
3.3. Results of Johansen Cointegration Test: Johansen (1988) and Johansen and Juselius (1990) developed cointegration methods in order to obtain long-run relationship among the series. According to Johansen cointegration test, non-stationary series, which are obtained from table 1, are tested whether these series reach long-run equilibrium. Table 1 shows Johansen cointegration test results.
Table 1. Johansen Cointegration Test Results
H0 H1 Eigenvalue Trace Statistic %5 Critical Value
Max-Eigen Statistic
%5 Critical Value
r=0 r≥1 0.247 46.38 35.19 28.68 22.30
=1
r r≥2 0.114 17.704 20.26 12.21 15.89
=2
r r≥3 0.053 5.49 9.165 5.49 9.165
Note. Sample: 1974-2005. No deterministic trend. Lags interval (in first differences): 1 to 1.
In the table 1, the null hypotehesis of r=0, there is no one cointegrating vector, is tested against the alternative . For the test based on the trace statistic, it is 35.2 so the null is rejected at 5% level, since the trace statistic is calculated as 46.38. In the case of maximum eigenvalue statistic, the critical value is 22.3 so that the null hypothesis can be rejected at the 5% level, since the maximum eigenvalue statistic is calculated as 28.68.
Thus, the result based on the trace statistic and maximum eigenvalue statistic represents that there is at least one cointegrating vector. The next step is to test the null hypothesis of against the alternative hypothesis of
1 r≥
1
r= r≥2, meaning there might be two
cointegrating vectors. In this case, the null cannot be rejected using either trace statistic (the 5% critical value is 20.3 while calculated value is 17.7) or the maximum eigenvalue statistic (the 5% critical value is 15.9 while the calculated value is 12.21), and so it can be concluded that there is exactly one cointegrating vector. Another step is to test the null of
=2
r against the alternative of . Here, the null cannot be rejected using either the trace statistic (the 5% critical value is 9.2 while the calculated value is 5.5) or the maximum eigenvalue statistic (the 5% value is 9.2 while the calculated value is 5.5, and again it can be concluded that there is exactly one cointegrating vector.
≥3 r
3. 4. Results of the Error Correction Models: Table 2 presents the results of the ECM which contains three equations and each equation includes adjustment coefficient.
Equation 1 takes into account of export (X) which has a negative adjustment coefficient (- 0.305), this is also significant (statistic is -3.1). Negative coefficient means there is a tendency from short term fluctuations to long term equilibrium condition. Thus, “no cointegrating hypothesis” can be rejected and alternative hypothesis is accepted.
Table 2. Error Correction Estimates
D(X) D(M) D(ER)
Adj. coefficient -0.305 0.249 0.0176
Standard Dev. (0.95) (0.255) (26.11)
t-statistic (-3.21) (0.975) (0.00067)
3.5. The Results of Variance Decomposition and Impulse Response Function: Variance decomposition gives information about the proportion of the movements in the dependent variables that are due to their own shocks, versus shocks to the other variables. A shock to any variable, for example a shock to export, will directly affect that variable (export), but this shock will also be transmitted to all of the other variables in the system (here imports and exchange rates) through the dynamic structure of the Vector Autoregression (VAR). Variance decomposition determine how much of the s-step-ahead forecast error variance of a given variable is explained by innovations to each explanatory variable for s
= 1, 2,…(Brooks, C., pp.342).
The first part of the table A3 shows the variance decomposition of exports following a shock to export innovations of $ 274 million. In the first round, the entire change in export is explained only by a shock to the export innovation. This shock also causes an immediate change in import and exchange rate, but the resulting changes in these variables have no effect on export at this time, since current imports and exchange rates
have no effect on current exports. In round two, exchange rate variables accounts for 0.15% of the change in exports, however, import accounts for 0.38% of the change in exports. When the entire 10-year period is taken into account, the effect of exchange rate on export, following the initial shock to the export innovation, is negligible, never getting larger than 3%, but the effect of imports on exports is getting larger than 50% after the 10-year period.
The second part of the table A3, which traces the variance decomposition of imports, presents reactions following a shock to import innovations of $ 701.69 million. Because of the ordering exports-imports-exchange rates, a shock to import innovations has an immediate effect on imports and current export which has a negligible impact on current imports at this point. In round one, a shock to the import innovation accounts for 99% of the variation in the import variable, while export accounts for the rest of the variation. In round two, export accounts for 9% of the variation in imports and imports itself account for 91% of its own variation. The influence of exports on imports increases in time and by the end of the 10-year period it is accounting for 15% of the total variation in imports.
Imports accounts for 83% of its own variation in the round 10.
The third part of the table A3 shows the variance decomposition of exchange rate following a shock to the exchange rate innovations of $71692.67 million. In the first round, a shock to the exchange rate innovation accounts for almost the entire change in exchange rate variation (99.63%). This situation is almost the same for all period, for example, at the end of the 10-year period, the variation in exchange rate because of the initial shock is accounted by exchange rate itself (97.58%).
In sum, the variance decomposition results indicate that imports and exchange rate are the most exogenous variables as a high proportion of their shocks are explained by their own innovations compared with the contributions of own shocks to innovations for exports. As stated above, at the end of 10 years, the forecast error variance for import and exchange rate explained by their own innovations are 83.35% and 97.58%, respectively.
An alternative way of obtaining information regarding the relationship among the variables, here export, import, and exchange rate, is via generalized impulse response functions. Figures A1-A3 in the Annex present impulse response functions.
3.6. The Direction of the Causality; Results from the Granger-Causality Test: The results of the Granger-causality test (GC) are presented in the table 3. The GC test results show that the null hypothesis of imports does not Granger cause exports is rejected at 1% level.
Also, there is “reverse causation” from X to M, since the F value is statistically significant. This conclusion also supports the variance decomposition results, which are presented in the Annex.
On the other hand, as stated before, there is no any association between exchange rate and exports and imports, the null hypothesis of exchange rate does not Granger cause export and imports cannot be rejected. In the table 3, there is a relationship between exchange rate (ER) and exports (X), but the null hypothesis of ER does not Granger cause X cannot be rejected since the F value is not statistically significant.
Table 3. Granger Causality Test
Direction of causality F-Statistic Probability
M ⇒ X 53.97 1.6E-16
X ⇒ M 5.78 0.0024
ER ⇒ X 1.39 0.253
X ⇒ ER 0.89 0.413
ER ⇒ M 0.37 0.695
M ⇒ ER 0.76 0.471
As seen in Guisan (2005) and (2006), the bilateral relationship between Imports and Exports is of uppermost importance for economic development. Exports increase the capacity to Import and besides real Imports have usually an important positive impact on industrial and non-industrial real Gross Domestic Product. The estimated effect of an increase of 100 dollars in real Imports and Exports on real GDP was: 62 in Canada, 79 in Mexico, 76 in the pool of 14 European Union countries, 91 in Spain, 132 in Turkey and 140 in the USA. All these values are expressed in dollars at 2000 prices and exchange rates but in the case of the USA they are expressed at 1990 prices. This author recommends industrial development for those countries with low levels of foreign trade in order to induce direct and indirect positive effects on trade and real GDP per inhabitant. Although Turkey has experienced a high increase of industrial development and foreign trade per inhabitant for the period 2001-2006, we may state that the levels are still very low in comparison with other OECD countries, and that an increase in these variables will be positive for economic development in Turkey.
4. Conclusion
This paper examined cointegration and causality among exchange rate, export, and import, at current prices, using Turkish monthly data for 1998-2006 period extracted from Turkish Treasury. The results show that export (X) is cointegrated with import (M). It might be thought that negative trade balance (the difference between imports and exports) can be reversed in the long-run by increasing exports if imported goods bring new technology and a different entrepreneur spirit to home country. Therefore, exports and imports are cointegrated. This result is supported by cointegration test results; for example, Johansen cointegration test has concluded that there is exactly one cointegrated vector. Also, the result of the variance decomposition has presented a relationship between exports and imports since a shock to export or import can be explained by exports and imports. The direction of causality has supported cointegration between exports and imports because bi-directional causality has been found between these two variables.
Another result from this paper is that there is no relationship between exchange rate and trade at current prices. The variance decomposition test explains that the power of exchange rate to explain the change in exports and imports at current prices is not larger than 3% after the 9-year period. Exchange rate accounts for the change in itself only when there is a shock to exchange rate innovations.
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On line Annex at the journal website
Journal published by the EAAEDS: http://www.usc.es/economet/eaa.htm
Annex
The ADF test is based on the following regressions:
t n
i i t
t y yi e
y = + + ∆ +
∆
∑
=
− 1 1 1
0
α α
α
(1)0 1 1
1 n
t t i
i
y
α α
y−α
yiδ
=
∆ = + +
∑
∆ + t+et (2)where y is a time series, is a linear time trend, t ∆ is the first difference operator,
α
0 is a constant, n is the optimum number of lags on the dependent variable, and is random error term. The difference between the equation 1 and 2 is that the first equation includes just drift, however, the second one includes both drift and linear time trend. The null hypothesis for testing nonstationarity ise
0 : 1
0
α
=H , meaning economic series are nonstationary. That is is a random walk and it has a unit root. If the t-statistic associated with estimated coefficient, here
yt
α
1, is less than the critical values fort the test, the null hypothesis of no-cointegration cannot be rejected at 1 or 5 or 10 % level of significance.This study also employs the PP test since the possibility of the presence of structural breaks makes the ADF test unreliable for testing stationarity. The presence of structural breaks will tend to bias the ADF test towards non rejection-of the null hypothesis of a unit root. Therefore, this paper has used both Phillips-Perron test (PP) suggested by Phillips and Perron (1988) and the ADF test to examine the sationarity of the data, whereas, other studies have used either the ADF or the PP test.
Table A1. Results of the ADF and PP unit roots tests Variable Augmented Dickey- Philips-Perron test (ADF) Fuller test (PP)test
Level First Level First
Form differences Form differences
X 1.494 -3.71 -0.131 -24.20
M 1.153 -13.85 -0.052 -21.096
ER -1.638 -9.08 -1.632 -9.085 Critical values at 1%, 5% and 10% significance levels: -3.50, -2.89 and -2.58.
Having established the hypothesis of non-stationarity for the underlying variables, here export, import, and exchange rate, the time series data will be examined for cointegration using the ADF cointegration tests. This paper will also employ Johansen’s cointegration test because, as stated by Afzal (2006), this test can estimate several cointegration relationship and fully captures the underlying time series properties of the data. Because of the number of cointegration vector is not known and only endogenous
variables are need to be known, using of the one equation model is restricted. However, Johansen (1988) has extended the technique of vector autoregression (VAR) model by which it is possible to identify the correct cointegration among the series.
If series are cointegrated, the standard Granger bivariate causality test will be performed. Estimating the following equations performs the standard Granger causality test:
t i i t i
t i
o X M
X =
α
+∑ α − +∑ δ − +µ
(3)
µ
(3)t i t i i
t
iM X e
M =
β
0+∑ ϕ − +∑ β − + (4)
According to equation 3, one variable M is said to Granger cause another variable, X, if X can be explained by using past values of X. Here, X (export) and M (import) are two separate economic time series. Equation 4 also represents the same relationship with equation 3. Causality can be found by testing the null hypothesis H0 =
δ
i =β
i =0. Ifi
iand
β
δ
are significant, there will be bi-directional causality. On the other hand, X Granger causes M ifβ
i is statistically significant butδ
i is not; and X Granger causes M ifδ
i is statically significant andβ
i is not. This is called unidirectional causality.However, Granger (1988) states that the standard Granger or Sims tests are likely to provide invalid causal inferences when the time series are cointegrated. This is because error correction model must be used instead of standard Granger-causality test. As to Engle and Granger (1987), the usual ECM technique may take the following form:
0 1 1
1 1
m m
t t t i i t i
i i
X
γ
e−γ
X − M− t= =
∆ =
∑
∆ +∑
Φ ∆ +V (5)where denotes first difference operator, is the error correction term, is the number of lags to obtain white noise and is another random disturbance term. If the coefficient of the error correction term is significantly different from zero
∆ et−1 m
Vt
2, in and this case, this will suggest that both series, Xt and Mt, exert a long-run relationship.
Table A2. Results of ADF cointegration tests
Regression Test statistic R- D.W Test R- D.W.
equation (Coefficient) square statistic square (residuals) M on X 0.63 0.94 0.897 2.81 0.30 1.95 ER on X 0.003 0.25 0.165 1.59 0.59 1.98 ER on M 85.80 0.25 0.061 1.70 0.03 2.28
Critical values at 1%, 5% and 10% significance levels: -3.50, -2.89 and -2.58.
2 The absolute value of the error correction term is taken into account.
Figure 1. Impulse response of export (X) to a one-standard deviation shock in export, import (M), and exchange rate (EX)
Figure 2. Impulse response of import to a one-standard deviation shock in import, export, and exchange rate
Figure 3. Impulse response of exchange rate to a one-standard deviation shock in exchange rate, export, and import
Figure 1 plots the response of export to a shock in export, import, and exchange rate.
The most interesting result from the figure 1 is the response of export to a shock in import; export falls whenever import increases. Meanwhile the response of export to a shock in exchange rate is the opposite. After the first two years, export increases with exchange rate but it is stable after eight-year period.
Figure 2 plots the response of imports to a shock in import, export, and exchange rate.
In response to a shock in export, import declines, the response of imports to a shock in export is similar to figure 1. However, in response to a shock in exchange rate, import is not affected. Figure 3 plots the response of exchange rate to shocks in export, import, and exchange rate. Own shocks explain the variation in exchange rate, but shocks in export and import slightly affect variation in exchange rate.
Table A3. Decomposition of variance (percentage of forecast variance explained by innovations)
Variance decomposition of exports
Period S.E. Export Import Exchange Rate
1 274.0310 100.0000 0.000000 0.000000
2 436.4124 61.78543 38.06609 0.148485
3 490.8136 53.07979 45.30394 1.616276
4 498.3658 51.49025 46.12929 2.380468
5 537.1145 46.35948 51.34991 2.290613
6 556.8961 46.09198 51.76619 2.141827
7 559.1063 46.41827 51.35888 2.222853
8 563.9369 45.74949 51.86492 2.385591
9 570.5303 45.50520 52.11066 2.384142
10 572.6948 45.62720 52.00136 2.371437
Variance decomposition of imports
Period S.E. Export Import ExchangeRate
1 701.6880 1.414009 98.58599 0.000000
2 836.9790 9.085517 90.77953 0.134948
3 855.1097 12.66319 87.19241 0.144398
4 879.4534 12.07767 87.19875 0.723576
5 908.1296 13.74971 85.27896 0.971326
6 919.0400 14.95954 84.08753 0.952930
7 920.3130 14.98649 83.91089 1.102621
8 926.1699 15.04676 83.76486 1.188382
9 931.8750 15.33210 83.48793 1.179971
10 932.9479 15.45690 83.35310 1.189998
Variance decomposition of exchange rate
Period S.E. Export Import ExchangeRate
1 71692.67 0.195557 0.176289 99.62815
2 72335.65 0.257923 0.345179 99.39690
3 72514.74 0.401079 0.496178 99.10274
4 72834.13 0.622502 1.142002 98.23550
5 72927.65 0.728958 1.280763 97.99028
6 72948.32 0.731068 1.322596 97.94634
7 73026.57 0.767171 1.488329 97.74450
8 73074.87 0.826510 1.557681 97.61581
9 73081.06 0.840780 1.557846 97.60137
10 73091.12 0.842305 1.579155 97.57854
