The JCurve Effect on the Trade Balance in Armenia
Gevorg Grigoryan
Shanghai University of Finance & Economics, School of Finance, Shanghai, China
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To cite this article:
Gevorg Grigoryan. The JCurve Effect on the Trade Balance in Armenia. International Journal of Economics, Finance and Management Sciences. Vol. 3, No. 3, 2015, pp. 270278. doi: 10.11648/j.ijefm.20150303.23
Abstract: Over the past years the Armenian Dram has appreciated against their major trading partners’ currencies. Such appreciation has received mixed reactions. Some economists argue that the depreciation of currencies is a good stimulant for export growth, while others argue that the net benefits of depreciation cannot outweigh its ills on the economy. The purpose of this paper is to investigate the effects of depreciation on the trade balance of Armenia using vector error correction model (VECM) and to trace the response of the trade balance to the shocks in the exchange rate using Impulse response function. This study finds evidence of the Jcurve on the Armenian trade balance and the existence of a longrun equilibrium relationship among the variables. This suggests that following a real depreciation the trade balance will initially deteriorate, but will improve in the long run.
Keywords: Armenia, JCurve, Exchange Rate, Trade Balance, VECM
1. Introduction
Many economists and policy makers believe that currency devaluation gives advantages in international trade. When a country devaluates its currency, domestic export goods become cheaper relative to its trading partners resulting in an increase in quantity demanded. The devaluation policy is mainly aimed at improving the trade balance. However, there is a time lag before the trade balance improves followed by devaluation. The short run and long run effects of depreciation on the trade balance are different. Theoretically, the trade balance deteriorates initially after depreciation and some time along the way it starts to improve, until it reaches its longrun equilibrium. The time path through which the trade balance follows generates a Jcurve. The time lag comes about, because of the time needed for recognition, decision, delivery, replacement and production (Junz and Rhomberg 1973). Following a real depreciation, traders take time to recognize the changes in market competitiveness, and this may take longer in international markets than in domestic markets, because of distance, language and cultural problems. Some time is spent on deciding on what business relationships to venture into and for the placement of new orders. Also, there is a delivery lag that explains the time taken before new payments are made for orders that were placed soon after the price shocks occurred. Procurement of new materials may be delayed to allow inventories of materials to be used up, which is called a replacement lag. Finally, there is a production lag before which producers become certain that the existing market condition will provide a profitable opportunity. One explanation for the Jcurve phenomenon is that the prices of imports rise soon after real depreciation but quantities take time to adjust downward because current imports and exports are based on orders placed some time before (Yarbrough and Yarbrough 2002).
On the other hand, domestic exports become more attractive to foreign markets, but quantities do not adjust immediately for the same reason. An increase in value of imports against a constant or a small change in the value of exports results in a trade deficit in the short run. As time passes by, importers have enough time to adjust their import quantities with respect to the rise in prices, while quantity demanded for exports increases, which results in an improvement in the trade balance. The longrun improvement in the trade balance occurs when the MarshallLerner condition holds. In the longrun the volume effect dominates the price effect of a real depreciation. In order for the trade balance to improve the sum of imports and exports demand and supply elasticities must be greater than unity.
In this study we attempt to investigate the response of the trade ratio to shocks in the real effective exchange rate in Armenia. To my knowledge there is no previous study on this topic in Armenia. The quarterly data from 2000:1 to 2010:3 is used. The paper employs the cointegration methodology to estimate the longrun equilibrium relationship between the trade ratio and the real exchange rate. The impulse response analysis and the vector error correction models are used to investigate the shortrun and feedback effects of the shocks in the exchange rate on the trade balance. The model sets trade balance as a function of real exchange rate, domestic income and foreign income. In this paper we define the trade balance as the ratio of imports to exports. This representation allows us to take the natural logarithm of the trade ratio, without worrying about negative values in case of trade deficits. The real effective exchange rate is used instead of the bilateral real exchange rate and weighted average of trading partners' real income is used as a proxy for foreign income.
The paper proceeds as follows, Section 2 provides the literature review. Section 3 develops theoretical framework and the empirical model. Section 4 provides the analysis of the empirical results. Section 5 concludes this paper.
2. Literature Review
During last few decades a large number of studies examined short run and long run relationship between exchange rate and trade balance. There are numerous empirical studies exploring both trade balance improvement after currency depreciation and the existence of Jcurve pattern. These studies covered a very diverse set of economies such as developed countries (US, Canada and Japan), number of emerging European and Asian economies, as well as few developing African countries. The Jcurve phenomenon was first advocated by Stephen P. Magee (1973) after the fact that the trade balance resembled the letter "J" in response to the currency depreciation.
The effects of devaluation of the exchange rate on the trade balance are related to the determinants of the demand and supply elasticities of exports and imports. BahmaniOskooee (2004) states, that in the short run, the elasticities are relatively smaller (inelastic demand and supply), than in the long run (elastic), hence the trade balance may deteriorate in the short run. Because of the currency contracts, initially, the trade balance worsens as a result of a real depreciation since prices and traded quantities cannot be changed.
As this situation assumes that exports are invoiced in domestic currency and imports in foreign currency, Hsing (1999) proposed, that the degree of foreign and domestic producer’s price passthrough to consumers and the scale of supply and demand elasticities of exports and imports, determine the value of the effect. The Jcurve effect can be explained by both a perfect passthrough and a zero passthrough. Under a perfect passthrough domestic import prices increase, while domestic export price remains unchanged. This results in deterioration in the trade balance. In zero passthrough situation, domestic export prices increase and domestic import prices remain constant hence the real trade balance improves following devaluation. Most studies on the Jcurve effect have come up with mixed results. Some results are consistent with the Jcurve phenomenon, while others show nonexistence or new evolution of the Jcurve effect.
GuptaKapoor and Ramakrishnan (1999) used the error correction model and the impulse response function to determine the Jcurve effect in Japan using quarterly data from 1975:1 1996:4. Their analysis showed the existence of the Jcurve on the Japanese trade balance.
Tihomir Stucka (2004) found evidence of Jcurve on trade balance of Croatia. His study employed a reduced form model to estimate the impact of a permanent shock on the merchandise trade balance. It was found that 1 percent depreciation in the exchange rate improves the equilibrium trade balance by the range from 0.94 to 1.3 percent, moreover it took 2.5 years for equilibrium to be established.
Koch and Rosensweig (1990) studied the dynamics between the dollar and components of U. S. trade. They employed time seriesspecification tests and Granger causality test to identify the Jcurve phenomenon. Two of the four components portrayed dynamic relationships that are weaker and more delayed than the standard Jcurve. The conventional Jcurve theory asserts a strong and rapid dependency of imports prices on the currency.
Carter and Pick (1989) found empirical evidence indicating the existence of the Jcurve using the U.S. Agricultural trade balance. The results exhibited deterioration in the trade balance that lasted for about 9 months following 10 percent depreciation in the U.S. dollar.
Hsing (2003) used the generalized impulse response function from the vector error correction model to examine the existence of Jcurve in Japan, Korea and Taiwan. He finds that Japan’s aggregate trade provided evidence of the phenomenon, while Korea and Taiwan did not show any presence of the Jcurve effect. He argues that this might be attributed to a small open economy effect. In small open economies like Korea and Taiwan, both imports and exports are invoiced in foreign currency as a result the short run effect of real devaluation is hedged and the trade balance remains unaffected.
Scott Hacker and Abdulansser HatemiJ (2004) used bilateral trade data to estimate the short and longrun effect of exchange rate changes on the trade balance in the transitional Central European economies of Czech Republic, Hungary and Poland against their trade with Germany. Their findings suggest that in all the three cases, there were some evidence of the Jcurve effect after real depreciation of the currencies in question. They also investigated the Jcurve effect replacing the real exchange rate with the nominal exchange rate and the relative German price level. The argument for introducing these variables is that real exchange rate changes are either a result of shocks in the nominal exchange rate or general domestic price changes. In some case it’s a combination of both variables. Nominal exchange rate changes are much more observable than real exchange rate changes. Besides, it is easily controlled by authorities. They found weak forms of the Jcurve effect.
Paresh Narayan (2004) investigates the Jcurve effect of on the trade balance for New Zealand. He finds no cointegrating relationship between the trade balance and real effective exchange rate, domestic income and the foreign income during the period of 19702000. However, the New Zealand trade balance exhibited a Jcurve pattern. Following a real depreciation of the New Zealand dollar, the trade balance worsens for the first three years and improves thereafter. Similar study by Wilson and Kua (2000) investigated the Singapore’s trade relations with the U.S. and found no significant impact of the Singapore’s real exchange rate on the trade balance and little evidence of the Jcurve hypothesis. This study used the partial reduced form model of Rose and Yellen (1989) derived from twocountry imperfect substitute model.
BahmaniOskooee et al. (2003) conducted another study on India’s trade balance and did not find any significant results. As researchers have long been argued that the problem could probably be the use of aggregated data, they used disaggregated data to investigate the Jcurve hypothesis against India’s trading partners. The empirical results of the study did not support the Jcurve pattern, but the longrun real depreciation of India’s Rupee had significant effect on the improvement of the trade balance. This clearly suggests that in studying the Jcurve phenomenon, it is crucial to separate and identify both the short and longrun implication of devaluation on the trade balance. In estimating the Jcurve, researchers either use aggregated or bilateral trade data. Rose and Yellen (1989) argue that the use of bilateral data is useful because a proxy for the world income variable is not required as in the aggregate analysis, which reduces aggregation bias.
3. Methodology
3.1. Theoretical Framework
In this work we follow the model introduced by Rose and Yellen (1989), where the country’s trade balance is a reduced form function of real exchange rate, real domestic and foreign incomes.
We start with a standard model specifying export and import demand functions
(1)
(2)
where and are the volume of exports and imports at time , is the nominal exchange rate, and are the domestic and foreign prices, and are domestic and foreign incomes. and are the real exchange rate elasticities for exports and imports and and are the income elasticities of exports and imports.
Rewriting equations (1) and (2) in logarithmic form,
(3)
(4)
where is the natural logarithm of real exchange rate. Let be the trade balance and is defined as the ratio between exports and imports
(5)
where . The coefficient of indicates whether MarshallLerner condition fulfilled or not. Note that and are assumed to have negative sign and and positive. MarshallLerner condition holds whenever has a positive sign, indicating that higher real exchange rate, or in other words the depreciation, improves the trade balance.
3.2. The Model
As we mentioned in the previous section we use partial reduced form model of Rose and Yellen (1989), which is as follows:
(6)
Instead of using trade balance as the dependent variable, we use the ratio of imports and exports (). The use of imports to exports ratio as a dependent variable has its advantages over the trade balance, so that we can take natural logarithms without worrying for the possible negative values of the trade balance in case of trade deficit as in Hsing (2003). The empirical model for trade balance of Armenia
(7)
Where is the logarithm of real imports to real exports ratio, is the logarithm of trade weighted real effective exchange rate, defined as the amount of domestic currency per unit of foreign currency, is the logarithm of weighted average of trade partners’ real income and is the logarithm of domestic real income (GDP). is the regression constant, are the parameters to be estimated and is the error term. All the variables are in logarithmic form, so that the parameter estimates could be interpreted as elasticities.
According to the Jcurve hypothesis, an increase in the real effective exchange rate initially reduces the demand for the home country’s exports and increases its demand for imports, hence this initially leads to deterioration of the trade balance. So here we expect the coefficient to be negatively related to, that is. Furthermore, we expect the trade ratio to be positively related to the domestic real income and negatively related to foreign income. However, for instance, an increase in economic activity of a trading partner not only boosts its demand for imports from Armenia but also its exports to Armenia.
We use the Johansen procedure (Johansen (1995)) to test for the existence of longrun relationship between the trade ratio and the real effective exchange rate. Since it only accounts for the long run cointegration between the variables, in order to capture the short run effects as well, we use the vector error correction (VEC) model. For VEC model to be applicable all variables should be stationary. Therefore, we run an Augmented DickeyFuller (ADF) test and PhillipPeron (PP) test to check for existence of unit roots. If the variables do not contain unit roots we can express the equation (7) as a vector error correction model.
(8)
Where the denotes the first difference operator, provides information about the speed of adjustment coefficient, is a pure white noise term.
We then apply impulse response function derived from the VEC model to estimate the Jcurve effect. The impulse response function shows the response of current and future trade ratio to one standard deviation change in the real effective exchange rate.
3.3. Variables and Data Discription
In this part we present detailed description of the variables and the data. As mentioned previously the variables used in the empirical model are the imports and exports ratio, real effective exchange rate, domestic real income and Armenia’s trade partners’ real income. The data set covers the period from 2000 to 2010 in quarterly frequencies. To be consistent in the interpretation of the estimates, all the variables must to be in the same currency except for the real effective exchange rate, since it is only an index. Fortunately the provided data set was in the same currency, specifically in US dollars, so we had no need to convert it. The small sample size is associated with the Republic of Armenia being a rather young country. The main data source used in this study was provided by the Central Bank of Republic of Armenia^{1} and National Statistic Service of the Republic of Armenia. Next we describe the variables more detailed.
3.3.1. Imports and Exports Ratio (ME)
To calculate the imports to exports ratio we are using the values of Armenia’s imports and export expressed in U.S dollars.
(9)
the data for both variables was provided by the Central Bank of Armenia.
We can easily notice that the trade balance of Armenia is suffering from deficit starting form 1996. Armenia’s export volume increased between 2001 and 2008 followed by a significant fall starting at the beginning of the financial crisis in 2008. Since then, export volume has started a modest recovery. In late 2008, exports and overall external trade volumes started to decrease. In the same period imports had a dramatic increase till 2008. The deepest decline in exports and imports were recorded in May 2009 declining by 48% and 30% respectively. Starting from the second half of the year, recovery trends became visible.
3.3.2. Real Effective Exchange Rate (REER)
The real effective exchange rate for Armenian Dram is based on consumer price index. There was no specific reason for choosing the CPI based REER other than convenience in the availability of the data.
(10)
Where the nominal effective exchange rate of Armenian Dram, is is the price level in Armenia, is the price level in country , is the proportionally calculated weight, based on the ratio of foreign trade between trading partner and home country to the total amount of home country’s foreign trade. The increase in the real effective exchange rate indicates appreciation of the currency.
Despite the volatilities, Armenian Dram has been appreciating throughout time, specifically, starting from the 3^{rd} quarter of 2003.
3.3.3. Trading Partners’ Real Income (FY)
is calculated in following way. First of all, we obtain the data on gross export and import among Armenia and its 10 main trade partners, such as Russian Federation, Germany, USA, China, Turkey, Bulgaria, Georgia and so on. Using the official statistics we compute the average value for the gross imports and exports. Taking these computed values as weights, we calculate as a weighted average that is we multiply the weights for each country by its real GDP growth rate. On the final step we add all of this multiplied figures and as a result we get DY as a weighted average values. It is obvious from the graph that data is not stationary, that is it contains a time trend and will need to be differenced.
3.3.4. Domestic Real Income (DY)
We use the gross domestic product (GDP) of Armenia expressed in US dollars. We can see from the graph that starting form year 2000 the domestic income has shown a steady improvement, on average showing 10% growth annually. The global financial and economic crisis heavily affected the Armenian economy. After continuing high growth rates for the past 16 years, Armenia experienced economic decline in 2009.Specifically, the decline started in late 2008, with further deepening in the beginning of 2009 and came to end by the end of the year. As a consequence, Armenia recorded 6.9% growth rate in 2008 in contrast to expected much higher rate, while in 2009, the economy declined by 14.2%. Again there is an obvious trend in the series, which indicates existence of unit root in the series. The data was provided by the National Statistical Service of the Republic of Armenia (NSSA)
4. Empirical Analysis and Results
4.1. Unit Root Test
To test for unit root of the variables the Augmented DickeyFuller (ADF) test procedure and PhillipsPeron test are employed as described by Enders (1995). The ADF test compared to the ordinary DickeyFuller unit root test, allows the inclusion of lagged dependent variable terms in order to correct for serially correlated residuals. The regression model of ADF test is as follows
(11)
Where, and are parameter estimates and is the error term. The number of augmented lags is denoted by . The null hypothesis of the ADF in this specification is that (the data needs to be differenced to make it stationary) and the alternative hypothesis is that (the data is stationary and doesn’t need to be differenced). Plots of variables for Armenia exhibit a presence of a trend in the series. As a result, the unit root tests for these variables must include a constant and a time trend, as equation (12) applies. Thus, the following equation is used
(12)
where is time trend and is a parameter estimate. The null hypothesis is (the data needs to be differenced to make it stationary) and the alternative hypothesis is (the data is trend stationary).
Phillips and Perron (1988) developed a number of unit root tests that have become popular in the analysis of financial time series. The main difference between PhillipsPerron (PP) unit root tests and the ADF tests is how they deal with serial correlation and heteroskedasticity in the errors. The PP tests ignore serial correlation in the test regression, but the error term may be heterosedastic. The test regression for the PP tests is
(13)
The results for the test are provided in the following tables. Table 1 reports the for the unit root test at level
Variables  ADF stat.  LL  PP stat.  BW 
 2.45  0  2.43  4 
 2.33  1  2.11  3 
 0.80  3  1.58  4 
 0.83  2  0.27  4 
Notes: LL is the lag length selected using the Schwartz Information Criterion and BW is the bandwidth based on the Newey West Bandwidth. *, **and *** reflect the significance level at 1%, 5% and 10%
As we see from table 41 all variables appear to be statistically not significant, therefore all variables are nonstationary at the level form. Further, we run unit root tests again after differencing all variables at the first level. For all variables test statistics exceed the critical values, except DY, though its critical value is very close to the 10% significance level. The results for the tests are depicted in Table 2. The unit root characteristics of the variables have important implication when testing for cointegration of the variables in a specified empirical model. It is often, wrongly assumed that all variables in the error correction model (ECM) need to be of order one. However, a necessary condition to finding a cointegrating relationship among nonstationary variables is that only two of the variables have to be integrated of the order one (Hansen and Juselius, 1995). According to GuptaKapoor and Ramakrishnan (1999) economic relevance should be a key determinant of the system of the variables in the VECM and not the time series properties of the data.
Variables  ADF stat.  LL  PP stat.  BW 
 7.64*  0  7.66  4 
 5.56*  1  4.47  9 
 6.01*  2  6.28  13 
 2.92  1  2.13  0 
Notes: LL is the lag length selected using the Schwartz Information Criterion and BW is the bandwidth based on the Newey West Bandwidth. *, **and *** reflect the significance level at 1%, 5% and 10%
4.2. Cointegration Test
The Johansen procedure can estimate multiple cointegrating vectors. This approach works with the estimation of Vector of Autoregression (VAR) of the form
Where is a column of vector of endogenous variables, and are matrices of unknown parameters, is the error term. All longrun information about the relationship between variables is contained in the impact matrix . When the matrix Π has full column rank, it implies that all variables in X are stationary. When the matrix Π has zero rank, the system is a traditional firstdifferenced VAR involving no longrun elements. However, when the rank of Π is intermediate or 0 < rank there exist cointegrating vectors that make the linear combinations of become stationary or cointegrated.
The one of the most important stage of the analysis is the selection of lag length. Lag lengths have significant influence on the outcome of the results. There is always a tradeoff between using too many lags and too few lags. Too many lags tend to make the model less parsimonious and reduce the degrees of freedom while using very few lags leads to correlation of the residuals. The Likelihood ratio, Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) indicated the optimal lag length be one.
We applied the Johansen procedure to test for cointegration of the variables. The Johansen method uses the maximum eigenvalue statistics and the trace statistics to determine the rank of the cointegrating vectors. The results of the Johansen cointegration rank test are presented in Table 3 and Table 4.
Hypothesized  Trace  0.05  
No. of CE(s)  Eigenvalue  Statistic  Critical Value  Prob.** 
None *  0.623619  80.98260  63.87610  0.0010 
At most 1  0.416019  40.91933  42.91525  0.0781 
At most 2  0.278274  18.86598  25.87211  0.2888 
At most 3  0.125442  5.495501  12.51798  0.5269 
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level. * denotes rejection of the hypothesis at the 0.05 level
Hypothesized  MaxEigen  0.05  
No. of CE(s)  Eigenvalue  Statistic  Critical Value  Prob.** 
None *  0.623619  40.06327  32.11832  0.0043 
At most 1  0.416019  22.05335  25.82321  0.1457 
At most 2  0.278274  13.37048  19.38704  0.2991 
At most 3  0.125442  5.495501  12.51798  0.5269 
Maxeigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnonHaugMichelis (1999) pvalues
As we see from the tables there is only one cointegration equation existing, since both Trace and Maximum Eigenvalue statistics exceed its’ critical values, so the null hypothesis of no cointegration against alternative hypothesis of existence of cointegration is strongly rejected at 5% significance level for both test.
The existence of one cointegrating vectors implies that the longrun relationship is unique. The results for long run relationship between the variables are presented in table5.
Variables  
Coeficients  1.000000  2.979856  24.41515  15.28627  34.51286 
Standard error  (0.90730)  (3.69334)  (3.21760)  
tstatistics  [3.28430]  [ 6.61058]  [4.75083] 
The long run cointegration equation can be written in following way,
The overall performance of variables is fairly good, that is all the variables are statistically significant. In Armenia, the long run model predicts negative relationship between trade ratio and real effective exchange rate, which is consistent with the theory that real depreciation improves the trade balance in the long run. However, the coefficients on domestic and foreign income have inconsistent signs to those predicted by economic theory. In our results domestic and foreign incomes are negatively and positively related to the trade ratio.
The coefficient of the real effective exchange rate () has a correct sign and it is statistically significant at 1% significance level. The implication of this relationship is that the appreciation of the real effective exchange rate by one point will have a negative impact on the by 2.979856. Since it is imports to exports ratio the trade balance will improve in the long run as the fraction will decrease.
The positive sign of states that the increase of Armenia’s main trade partner countries’ income has a negative impact on the trade balance. The tstatistics shows significance at 1% level. Similarly, because of the negative sign, an increase in domestic income will be followed by the improvement in the trade ratio, since, the exports will increase.
Further, to estimate the relationship between the trade ratio and its determinants in the short run, we run the regression using the vector error correction model VECM (table 6).
Variables  
Coefficient  0.077296  0.002960  0.055901  0.014180 
Standard error  (0.05248)  (0.01796)  (0.01353)  (0.00514) 
tstatistics  [ 1.47283]  [ 0.16483]  [4.13181]  [2.75615] 
In the short run real effective exchange rate has positive impact on the trade balance, where denotes the first difference of the variable. However, it is not significant. Both the foreign income and domestic income have negative signs and are statistically significant at 1% significance level.
4.3. Variance Decomposition
The essence of a variance decomposition analysis is its ability to provide information about the relative importance of the random innovations. Specifically, it provides information on the percentage of variation in the forecast error of a variable explained by its own innovation and the proportion explained by innovations in other variables. Table 7 summarizes the results of the variance decomposition on the effects of trade balance, real effective exchange rates, foreign income and domestic income on the trade balance.
Period 




1  100.0000  0.000000  0.000000  0.000000 
2  99.40684  0.023184  0.250842  0.319137 
3  97.83992  0.313943  0.960888  0.885249 
4  95.99598  0.5m93894  1.336518  2.073603 
5  93.86878  0.839328  1.476812  3.815082 
6  91.58268  1.017577  1.441694  5.958048 
7  89.24415  1.125938  1.322816  8.307096 
8  86.97810  1.177717  1.190390  10.65380 
9  84.89346  1.189552  1.081629  12.83536 
10  83.06513  1.176918  1.006798  14.75115 
The variance decomposition suggests that the significant part of the variability in the forecast error for trade balance is explained by its own innovations, which accounts for about 92% and declines over time. The results demonstrate that current performance of trade balance relays largely on past performance. Only 5.95% and 1.5% of the variability in forecast error for trade balance is explained by domestic and foreign income, respectively. Furthermore, the empirical results suggest that only a small proportion of variability in trade balance could be attributed to innovations in the real effective exchange rates.
4.4. Impulse Response
Recent studies suggest that the best way of deriving evidence of the jcurve is to use impulse response function. An impulse response function measures the time profile of the effect of shocks at a given point of time on the expected (future) values of variables in a dynamic system. The best way to describe an impulse response is to view it as the outcome of a conceptual experiment in which the time profile of the effect of a hypothetical vector of shocks of size , say, hitting the economy at time is compared with a baseline profile at time , given the economy's history. After giving a residual shock by one unit we obtain the following figure.
Figure 6 shows a clear path of inverse Jcurve, it is inversed because of the definition of trade balance. The figure shows that in short run Armenia’s trade ratio initially deteriorates by about 10% due to a 1% real depreciation in Armenian currency.The deterioration of the trade ratio is due to price effect which implies that the unit value of imports has increased resulting in an increase in total value of imports against a constant or an insignificant change in the value of exports. About four quarters after the initial shock, the trade ratio starts to improve. The improvement in the trade balance is due to volume effect, as both supply and demand elasticities increase in absolute values.
Much as we find evidence of the Jcurve hypothesis in Armenia, this does not provide enough information to prescribe a devaluationary policy in Armenia. It is important to assess the effect of such policy on the economy as a whole and not just the trade balance. It is possible to observe the trade balance improvement as a result of real depreciation at the same time register a decline in gross national product. The net effect could be zero as the improvement in the trade balance would be offset by the decline in the gross national product. We should consider the behavior of other macroeconomic variables such as interest rate, inflation rate and so on under the devaluationary policies. One needs to keep in mind that devaluation has its own contractionary effects on the economy. Devaluation raises the cost of imported intermediate inputs and this affects supply side of the economy. In situations where devaluation is accompanied by inflation in the domestic market, it erodes purchasing power of money resulting in a decline in the aggregate demand.
5. Conclusion
This paper employed cointegration analysis and vector error correction model to investigate the Jcurve effect on the trade balance in the Republic of Armenia. Negative relation between trade ratio and the real effective exchange rate was found from the longrun equilibrium model for Armenia. From theoretical point of view this implies that real currency depreciation will lead to a longrun improvement in the trade ratio. It states that exports increases more than imports and the trade balance is expected to be positive. The empirical results of the impulse response function from the vector error correction model suggest that Armenia support the Jcurve hypothesis that soon after a real depreciation, the trade balance deteriorates as a result of price effects. The unit value of imports increases relative to exports but as time passes by, the volume effect takes over and the trade balance starts to improve. One limitation of this study is the short time series data. Finally, we will be very careful to prescribing devaluationary policy in the Republic of Armenia. Countries planning to implement policies targeted at the exchange rate need to do that with caution because of varying macroeconomic environments and different responses to currency depreciation.