Download A Study of the J-Curve for Seven Selected Latin American Countries

Hsing, Global Economy Journal, 2008, 8(4), forthcoming
1
A Study of the J-Curve for Seven Selected
Latin American Countries
Yu Hsing*
Southeastern Louisiana University
Abstract This study finds that there is evidence of a J-curve for Chili, Ecuador, and Uruguay
and lack of support for a J-curve for Argentina, Brazil, Colombia, and Peru. Increased real
income in the home country would improve the trade balance for Brazil and Ecuador and
deteriorate the trade balance for Argentina, Chili, Colombia, Peru, and Uruguay. Increased real
income in the U.S. would improve the trade balance for Argentina, Chile, Colombia, Peru, and
Uruguay and deteriorate the trade balance for Brazil and Ecuador. Hence, the conventional
wisdom to pursue real depreciation to improve the trade balance may not apply to some of these
countries.
Keywords: J-curve, trade balance, real exchange rate, VECM, generalized impulse response
function
JEL Classification: F14, F31
1. Introduction
This paper examines the J-curve for seven selected South Latin American countries. A J-curve
postulates that after real depreciation or devaluation, the trade balance is expected to deteriorate
at first, then improve because the increased value of imports initially would dominate the
increased volume of exports, and increased volume of exports would outweigh the increased
value of imports later. The analysis is important in light of the recent South American
experience. For example, during 1997-2002, the Brazilian real per U.S. dollar experienced real
depreciation, declining from 1.164 to 2.633. The trade balance improved from a deficit of 20,658
millions to a surplus of 22,369 millions, and the ratio of exports to imports rose from 75.61% to
112.03%. During 2002-2007, the Brazilian real per U.S. dollar experienced real appreciation and
changed from 2.633 to 1.430. However, the trade balance continued to increase from 22,369
millions to 39,112 millions, and the ratio of exports to imports changed little from 112.03% to
112.38%. Our analysis suggests that there is no J-curve for Brazil which is consistent with their
experience of no real difference between the long and short run adjustments.
During 2001-2002, Argentina’s real exchange rate in terms of the peso per U.S. dollar
depreciated considerably from 1.039 to 2.570. As a result, the trade balance improved from
3,509 millions to 46,926 millions, and the ratio of exports to imports increased from 112.71% to
212.28%. During 2002-2007, Argentina’s peso per U.S. dollar experienced real appreciation
Hsing, Global Economy Journal, 2008, 8(4), forthcoming
2
from 2.570 to 1.910. The trade balance declined to 34,866 million, and the ratio of exports to
imports decreased to 121.21%. Our study indicates that there is lack of support for a J-curve for
Argentina due to positive adjustments in the short run and negative adjustments in the long run.
The real effective exchange rate (an increase means real appreciation) for Colombia changed
from 82.54 in 1990 to 128.08 in 1997, 84.89 in 2003, and 103.65 in 2006. The trade balance
deteriorated from a surplus of 708.29 billions in 1990 to a deficit of 7,197.7 billions in 1997, a
deficit of 1,071.2 billions in 2003, and a deficit of 7,885.3 billions in 2006. It seems that the
recent real appreciation from 84.89 to 103.65 hurt net exports significantly. Our investigation
shows that there is lack of evidence of a J-curve for Colombia due to positive adjustments in the
short run and negative adjustments in the long run. Other countries show different responses of
the trade balance to real depreciation or appreciation.
A few points are worth noting. First, some of the previous studies rely on aggregate trade data of
total exports and total imports, rather than using bilateral trade data between two countries. The
use of bilateral trade data for each of the South American countries analyzed here and the U.S.
provides more detailed estimates. Second, most of previous studies use conventional regression
analysis to find parameter estimates and elasticities of the trade balances with respect to real
depreciation and other relevant variables, whereas this paper applies time series techniques such
as the vector-error correction model, the generalized impulse response function, and the
cointegrating equation to determine how the trade balance would react to a shock to real
depreciation in both the short run and long run. Third, this study uses the most recent bilateral
trade data in order to provide policy makers with up-to-date empirical results for consideration
and application in their decision-making processes.
The paper is organized as follows. The literature related to the J-curve for these countries is
surveyed in the second section. The theoretical model of the trade balance is presented in the
third section. Data sources and empirical results are described and analyzed in the fourth section.
A summary and conclusions are made in the last section.
2. Literature Survey
Economists have extensively examined the relationship between the real depreciation or
devaluation of a domestic currency and the improvement or deterioration of a country’s trade
balance. Miles (1979) shows that real devaluation does not improve the trade balance but
improves the balance of payment. He also demonstrates that there is lack of evidence of the Jcurve for Ecuador, Guyana, and 12 other countries. Himarios (1985) finds that real devaluation
improves the trade balance for Ecuador and 8 other countries. Rose (1990) examines the impact
of real depreciation on the trade balance for 30 developing countries and finds lack of evidence
that real depreciation would lead to an improved trade balance for Argentina, Brazil, Chile,
Colombia, Peru, and Uruguay.
Bahmani-Oskooee and Malixi (1992) find evidence of a J-curve for Brazil and lack of support
for a J-curve for Peru. They also find that several other shapes, such as the I-, M-, and N-curves,
characterize the response of the trade balance to real depreciation in the short run and that real
Hsing, Global Economy Journal, 2008, 8(4), forthcoming
3
depreciation would lead to improved trade balance in 8 countries, including Brazil and Peru, in
the long run. Bahmani-Oskooee and Alse (1994) study the J-curve effect for 22 LDCs and find
that there is lack of evidence of a J-curve for Argentina, Brazil, Colombia, and Ecuador.
Bahmani-Oskooee and Ratha (2004b) show that if a new definition (Rose and Yellen, 1989) of
the J-curve is adopted, there would be a J-curve in 7 out of 13 countries including Argentina and
Chile. Bahmani-Oskooee and Ratha (2004a) provide a detailed review of major previous studies.
3. The Model
Let’s denote P, P*, e, eP*, and E as export price in domestic currency, import price in foreign
currency, the domestic price of a unit of foreign exchange, import price in domestic currency,
and the real exchange rate or eP*/P. Applying and extending Rose and Yellen (1989) and Rose
(1990), exports, imports, the real trade balance can be expressed as:
X = X (E,Y * )
(1)
M = M (E,Y )
(2)
TB = X − EM = X ( E , Y * ) − EM ( E , Y )
(3)
or
TB = TB ( E , Y , Y * )
(4)
where
X
M
TB
Y*
Y
= exports,
= imports,
= the real trade balance,
= real income in the U.S., and
= real income in the home country.
The partial derivative of the real trade balance with respect to real depreciation is given by:
∂TB / ∂E = ∂X / ∂E − E∂M / ∂E − M > or < 0.
(5)
It can be shown that if TB = 0, equation (5) will be reduced to the Marshall-Lerner condition.
The sign of equation (5) depends on whether the volume effect of increased exports would be
greater or less than the value effect of imports (Krugman and Obstfeld, 2003).
The sign of ∂TB / ∂Y in equation (4) is unclear because higher real income in the home country
may increase imports, leading to a deterioration of the trade balance, or reduce imports due to
growth in import-substitute production. The sign of ∂TB / ∂Y * in equation (4) is also ambiguous
because higher real income in the U.S. may increase exports to the U.S. from her trading partners
Hsing, Global Economy Journal, 2008, 8(4), forthcoming
4
or reduce imports from her trading partners due to growth in import-substitute production in the
U.S.
To measure the elasticity of the trade balance with respect to the real exchange rate, real income
in the home country, and real income in the U.S., equation (4) can be expressed as a log-log
equation:
ln TBit = α 1 + α 2 ln Eij + α 3 ln Yit + α 4 ln Yt*
(6)
where α 2 measures the elasticity of the trade balance with respect to the real exchange rate, α 3
denotes the elasticity of the trade balance with respect to real income in the home country, and
α 4 represents the elasticity of the trade balance with respect to real income in the U.S.
4. Empirical Results
The data for the exchange rate, real income in the home country, real income in the U.S., and
relative prices were taken from International Financial Statistics, a publication of the
International Monetary Fund. Exports to and imports from the U.S. were collected from
Direction of Trade Statistics, also published by the International Monetary Fund. For Brazil,
nominal exports and imports are divided by the export and import price indexes and expressed in
real terms. The trade balance is measured as the log of real exports divided by real imports. The
real exchange rate is equal to the nominal exchange rate expressed as units of the home currency
per U.S. dollar times the price in the U.S. and divided by the price in the home country. Hence,
an increase in the real exchange rate is real depreciation of the home country currency. The
consumer price index is used for the price level. Real GDP in index number is selected to
represent real income in the home country or the U.S., with the exception that industrial or
manufacturing production index is used for Brazil and Uruguay due to lack of quarterly data for
real GDP. Venezuela and Paraguay are not included due to lack of quarterly data for real output
or industrial production. Sample periods vary by country and are reported in the table for
estimated cointegrating equations. The main reason why the same sample period is not used is
because statistical tests for some countries would be affected adversely due to relatively small
sample sizes. The unit root test indicates that all the variables have unit roots in level and are
stationary in first difference.
Table 1 presents the cointegration test based on the maximum eigenvalue test. As shown, the null
hypothesis that there is no cointegrating relationship for each of the countries can be rejected at
the 5% level.
Table 2 presents the estimated long-run cointegrating equations. As shown, the trade balance is
positively affected by real depreciation for Argentina, Brazil, Ecuador, Peru, and Uruguay,
negatively associated with real depreciation for Chile, and is not influenced by real depreciation
for Colombia due to the insignificant coefficients. Real income in the home country has a
positive impact on the trade balance for Brazil and Ecuador and a negative impact on the trade
balance for Argentina, Chile, Colombia, Peru, and Uruguay. Real income in the U.S. has a
Hsing, Global Economy Journal, 2008, 8(4), forthcoming
5
positive effect on the trade balance for Argentina, Chile, Colombia, Peru, and Uruguay and a
negative effect on the trade balance for Brazil and Ecuador.
Graph 1 presents the estimated generalized impulse response function for each of the countries.
There are several patterns. The first pattern is the J-curve that can be observed for Chile,
Ecuador, and Uruguay. In responding to real depreciation, it takes just one quarter for the trade
balance in Chile to change from a negative to a positive value, and it takes 2 quarters for the
trade balance in Ecuador to change from a negative value to a positive value. The second pattern
is the complete negative response of the trade balance to real depreciation for Peru. The third
pattern is the initial positive response of the trade balance to real depreciation and the negative
response later for Argentina and Colombia. The fourth pattern is the positive response (though
with the exception of some negative response) for Brazil in the fifth quarter.
In comparison, the results in this paper are different from Miles (1979) for Ecuador, similar to
Himarios (1985) for Ecuador in the long run, different from Rose (1990) for Argentina, Brazil,
and Uruguay and similar to Rose (1990) for Chile, Colombia, and Peru in the long run. The
findings of this paper are similar to Bahmani-Oskooee and Malixi (1992) for Brazil and different
from Bahmani-Oskooee and Malixi (1992) for Peru, similar to Bahmani-Oskooee and Alse
(1994) for Argentina, Brazil, and Colombia and different from Bahmani-Oskooee and Alse
(1994) for Ecuador, similar to Bahmani-Oskooee and Ratha (2004b) for Chile and different from
Bahmani-Oskooee and Ratha (2004b) for Argentina. Possible reasons for different results are the
use of different models, econometric methodologies, data, and sample periods.
5. Summary and Conclusions
This paper has examined the J-curve of the bilateral trade between the U.S. and her seven South
American trading partners including Argentina, Brazil, Chile, Colombia, Ecuador, Peru, and
Uruguay. The vector-error correction model and the generalized impulse response function were
applied and estimated in order to measure the response of the trade balance to a shock to real
depreciation. The cointegrating equation was estimated to determine the long-run relationship
between the trade balance and real depreciation, real income in the home country, and the real
income in the U.S.
Major findings are summarized as follows. The trade balance for Argentina has a positive
relationship with real depreciation and real income in the U.S. and a negative relationship with
own real income in the long run. There is lack of support for a J curve. The impulse response
function shows that real depreciation leads to a trade surplus in the first 7 quarters and a trade
deficit afterward. Therefore, in Argentina, real depreciation generates different impacts on trade
balance in the short run and in the long run.
The balance of trade for Brazil is positively associated with real depreciation and own real
income and negatively influenced by real income in the U.S. in the long run. There is lack of
support for a J curve. The impulse response function shows that real depreciation leads to a trade
surplus initially, a trade deficit in the fifth quarter, then a trade surplus afterward. Hence, real
depreciation would lead to improvement in the trade balance except for one quarter.
Hsing, Global Economy Journal, 2008, 8(4), forthcoming
6
For Chile, real depreciation is negatively affected by real depreciation and own real income and
positive influenced by real income in the U.S. in the long run. There is evidence of a J curve.
These results suggest that after real depreciation, there would be a trade deficit initially, then a
trade surplus afterward. Higher real income in the U.S. would also improve the trade balance.
The trade balance for Colombia is not affected by real depreciation, has a negative relationship
with own real income, and has a positive relationship with real income in the U.S. in the long
run. There is no evidence of a J curve because the trade balance improves initially and
deteriorates later. Thus, the positive impact of real depreciation on the trade balance is shortlived. Higher real income in the U.S. would improve the trade balance.
For Ecuador, real depreciation and higher own real income would improve the trade balance
whereas higher real income in the U.S. would deteriorate the trade balance in the long run. Like
Chile, there is support for a J curve. With real depreciation, the trade balance deteriorates in the
first quarter and improves afterward. Therefore, real depreciation may be a useful policy tool to
improve net exports.
The trade balance for Peru is negatively affected by real depreciation and own real income and
positively associated with real income in the U.S. in the long run. There is no evidence of a J
curve because real depreciation leads to a trade deficit. Hence, to improve the trade balance, real
appreciation instead of real depreciation should be considered.
Like Argentina, the trade balance for Uruguay is positively affected by real depreciation and real
income in the U.S. and negatively associated with own real income in the long run. Like Chile
and Ecuador, there is support for a J curve for Uruguay. Real depreciation would lead to
deterioration in the trade balance in the first four quarters and a subsequent improvement in the
trade balance.
In view of the broad range of empirical results, these countries may need to exert caution in
pursuing the exchange rate policy. For example, real depreciation would deteriorate the trade
balance for Peru in the short run and long run whereas real depreciation would improve the trade
balance for Ecuador after the first quarter and Uruguay after the 4th quarter.
There are may be areas for future research. In the selection process of the lag length, there may
be different empirical outcomes when the lag length is changed slightly due to the use of
different criteria. The outcomes of the generalized impulse response function and the
coingetrating equation may not be consistent partly due to the multicollinearity problem
(Bahmani-Oskooee and Malixi, 1992). The choice of different methods in estimating the impulse
response function may also lead to different results.
Hsing, Global Economy Journal, 2008, 8(4), forthcoming
7
Endnote
*
Department of Business Administration, College of Business, Southeastern Louisiana
University, Hammond, LA 70402. E-Mail: [email protected]. I appreciate insightful comments
from the referee and the editor. Any errors are the author’s sole responsibility.
References
Arora, S., M. Bahmani-Oskooee, and G. G. Goswami. 2003, “Bilateral J-curve between India
and Her Trading Partners,” Applied Economics, 35, 1037–1041.
Bahmani-Oskooee, M. 1985. “Devaluation and the J-curve: Some Evidence from LDCs,”
Review of Economics and Statistics, 67, 500–504.
Bahmani-Oskooee, M. and J. Alse. 1994. “Short-Run versus Long-Run Effects of Devaluation:
Error Correction Modeling and Cointegration,” Eastern Economic Journal, 20, 453–464.
Bahmani-Oskooee, M. and T. J.Brooks. 1999. “Bilateral J-Curve between US and Her Trading
Partners,” Weltwirtschaftliches Archiv, 135, 156–165.
Bahmani-Oskooee, M. and G. G. Goswami. 2003. “A Disaggregated Approach to Test the JCurve Phenomenon: Japan vs. Her Major Trading Partners,” Journal of Economics and Finance,
27, 102–113.
Bhmani-Oskooee, M. and T. Kanitpong. 2001. “Bilateral J-Curve between Thailand and Her
Trading Partners,” Journal of Economic Development, 26, 107-117.
Bahmani-Oskooee, M. and M. Malixi. 1992. “More Evidence on the J-Curve from LDCs,”
Journal of Policy Modeling, 14, 641-653.
Bahmani-Oskooee, M. and A. Ratha. 2004a. “The J-curve: A Literature Review,” Applied
Economics, 36, 1377-1398.
Bahmani-Oskooee, M. and A. Ratha. 2004b. “Dynamics of US Trade with Developing
Countries,” Journal of Developing Areas, 37, 1-11.
Bahmani-Oskooee, M. and A. Ratha. 2004c. “The J-curve Dynamics of U.S. Bilateral Trade,”
Journal of Economics and Finance, 28, 32-38.
Bahmani-Oskooee, M. and A. Ratha. 2007. “The Bilateral J-Curve: Sweden versus Her 17
Major Trading Partners,” International Journal of Applied Economics, 4, 1-13.
Boyd, D., G. M. Caporale, and R. Smith. 2001. “Real Exchange Rate Effects on the Balance of
Trade: Cointegration and the Marshall-Lerner Condition,” International Journal of Finance and
Economics, 6, 187-200.
Hsing, Global Economy Journal, 2008, 8(4), forthcoming
8
Gupta-Kapoor, A. and U. Ramakrishnan. 1999. “Is There a J-Curve? A New Estimation for
Japan,” International Economic Journal, 13, 71–79.
Hacker, R. S. and H.-J. Abdulnasser. 2003. “Is the J-Curve Effect Observable for Small North
European Economies?” Open Economies Review, 14, 119–134.
Hacker, R. S. and A. Hatemi-J. 2004. “The Effect of Exchange Rate Changes on Trade
Balances in the Short and Long Run: Evidence from German Trade with Transitional Central
European Economies,” Economics of Transition, 12, 777-799.
Himarios, D. 1985. The effects of devaluation on the trade balance: a critical view and
reexamination of Miles’s ‘new results’. Journal of International Money and Finance, 4, 553-563.
Junz, H. B. and R. R. Rhomberg. 1973. “Price-Competitiveness in Export Trade among
Industrial Countries,” American Economic Review, 63, 412–418.
Krugman, P. R. and M. Obstfeld. 2003. International Economics: Theory and Policy, 6th
edition, Reading, MA: Addison-Wesley.
Lal, A. K. and T. C. Lowinger. 2002. “The J-Curve: Evidence from East Asia,” Journal of
Economic Integration, 17, 397–415.
Lee, J. and M. D. Chinn. 2002, “Current Account and Real Exchange Rate Dynamics in the G7 Countries,” IMF Working Paper, No. WP/02/130.
Levin, J. H. 1983. “The J-Curve, Rational Expectations, and the Stability of the Flexible
Exchange Rate System,” Journal of International Economics, 15, 239–251.
Magee, S. P. 1973. “Currency Contracts, Pass Through and Devaluation,” Brooking Papers on
Economic Activity, 1, 303–325.
Marquez, J. 1990. “Bilateral Trade Elasticities,” Review of Economics and Statistics, 72, 70-77.
Meade, E. E. 1988. “Exchange Rates, Adjustment, and the J-Curve,” Federal Reserve Bulletin,
74, 633–644.
Miles, M. A. 1979. “The Effects of Devaluation on the Trade Balance and the Balance of
Payments: Some New Results,” Journal of Political Economy, 87, 600–620.
Noland, M. 1989. “Japanese Trade Elasticities and the J-Curve,” Review of Economics and
Statistics, 71, 175-179.
Onafowora, O. 2003. “Exchange Rate and Trade Balance in East Asia: Is There a J-Curve?”
Economics Bulletin, 5, 1-13.
Hsing, Global Economy Journal, 2008, 8(4), forthcoming
9
Pesaran, M. H. and Y. Shin. 1998. “Generalized Impulse Response Analysis in Linear
Multivariate Models,” Economics Letters, 58, 17-29.
Rose, A. K. 1990. “Exchange Rates and the Trade Balance: Some Evidence from Developing
Countries,” Economics Letters, 34, 271–275.
Rose, A. K. and J. L. Yellen. 1989. “Is There a J-Curve?” Journal of Monetary Economics, 24,
53–68.
Wilson, P. 2001. “Exchange Rates and the Trade Balance for Dynamic Asian Economies: Does
the J-Curve Exist for Singapore, Malaysia and Korea?” Open Economies Review, 12, 389–413.
Hsing, Global Economy Journal, 8(4), forthcoming
Table 1. The Johansen Cointegration Test
Hypothesized
Max-Eigen
0.05
No. of CE(s) Eigenvalue
Statistic Critical Value
Prob.**
Argentina (1,1)
None *
0.467888
At most 1
0.334271
At most 2
0.121705
At most 3
0.085842
35.96141
23.19172
7.397065
5.115835
32.11832
25.82321
19.38704
12.51798
0.0161
0.1072
0.8719
0.5798
Brazil (1,2)
None *
At most 1
At most 2
At most 3
0.523973
0.212876
0.116628
0.105526
37.11398
11.96845
6.200419
5.575983
32.11832
25.82321
19.38704
12.51798
0.0112
0.8747
0.9469
0.5159
Chile (1,1)
None *
At most 1
At most 2
At most 3
0.652437
0.136209
0.081889
0.055608
115.1921
15.96030
9.312577
6.236349
32.11832
25.82321
19.38704
12.51798
0.0000
0.5482
0.6920
0.4309
Colombia (1,3)
None *
0.471655
At most 1
0.392159
At most 2
0.161091
At most 3
0.134019
32.53832
25.38990
8.958314
7.338526
32.11832
25.82321
19.38704
12.51798
0.0444
0.0569
0.7287
0.3105
Ecuador (1,2)
None *
At most 1
At most 2
At most 3
0.512306
0.243934
0.206790
0.064805
45.95634
17.89611
14.82671
4.288042
32.11832
25.82321
19.38704
12.51798
0.0006
0.3852
0.2032
0.7001
Peru (1,2)
None *
At most 1
At most 2
At most 3
0.629025
0.234856
0.106288
0.089110
59.49729
16.06151
6.742270
5.600001
32.11832
25.82321
19.38704
12.51798
0.0000
0.5392
0.9174
0.5127
Uruguay (1,2)
None *
At most 1
At most 2
At most 3
0.535365
0.290210
0.147145
0.072117
43.69074
19.53880
9.072458
4.266419
32.11832
25.82321
19.38704
12.51798
0.0013
0.2705
0.7170
0.7033
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
10
Hsing, Global Economy Journal, 8(4), forthcoming
11
Table 2. Estimated Cointegrating Equations by Normalizing on ln TB
Country
ln E
ln Y
ln Y*
Constant
Sample period
Argentina
0.514
(3.239)
-2.522
(-5.752)
3.061
(5.255)
-3.004
1994.Q2-2007.Q3
Brazil
27.820
(3.565)
250.169
(4.633)
-236.843
(-4.564)
-84.419
1995.Q3-2007.Q3
Chile
-1.060
(-1.897)
-5.274
(-4.626)
9.876
(4.531)
-14.258
1980.Q4-2007.Q3
Colombia
0.129
(0.571)
-3.559
(-5.289)
4.083
(6.700)
-2.976
1995.Q1-2007.Q3
Ecuador
0.979
(5.285)
5.129
(6.813)
-3.306
(-5.926)
-2.914
1991.Q4-2007.Q3
Peru
-17.282
(-5.628)
-21.840
(-5.555)
39.402
(6.168)
-59.097
1992.Q3-2007.Q1
Uruguay
1.660
(5.156)
-3.099
(-3.564)
3.053
(4.618)
-4.253
1993.Q1-2007.Q3
Notes:
The dependent variable is the trade balance (TB) defined as log(X/M), where X and M are exports and
imports.
E is the real exchange rate.
Y is real income in the home country.
Y* is real income in the U.S.
Figures in the parenthesis are t statistics.
Hsing, Global Economy Journal, 8(4), forthcoming
12
Graph 1. Impulse Response Function
Argentina
Uruguay
Colombia
Response of LOG(TB) to Generalized One
S.D. LOG(E) Innovation
Response of LOG(TB) to Generalized One
S.D. LOG(E) Innovation
Response of LOG(TB) to Generalized One
S.D. LOG(E) Innovation
.08
.04
.30
.06
.03
.25
.04
.02
.20
.01
.15
.00
.10
-.01
.05
.02
.00
-.02
-.04
2
4
6
8
10
12
14
16
18
20
Brazil
-.02
.00
-.03
-.05
2
4
6
8
10
12
14
16
18
20
Ecuador
Response of LOG(TB) to Generalized One
S.D. LOG(E) Innovation
Response of LOG(TB) to Generalized One
S.D. LOG(E) Innovation
.08
.08
.07
.06
.06
.05
.04
.04
.03
.02
.02
.00
.01
.00
-.02
2
4
6
8
10
12
14
16
18
20
Chile
-.01
2
4
6
8
10
12
14
16
18
20
Peru
Response of LOG(TB) to Generalized One
S.D. LOG(E) Innovation
Response of LOG(TB) to Generalized One
S.D. LOG(E) Innovation
.10
-.05
-.06
.08
-.07
.06
-.08
-.09
.04
-.10
.02
-.11
-.12
.00
-.13
-.02
2
4
6
8
10
12
14
16
18
20
Notes:
TB = the trade balance, and
E = the real exchange rate.
-.14
2
4
6
8
10
12
14
16
18
20
2
4
6
8
10
12
14
16
18
20