(Luận văn thạc sĩ) tác động của tỷ giá thực lên cán cân thương mại việt nam

BQ GIAO DVC VA I>AO T~O TRUONG 1>~1 HQC KINH TE TP.HCM MAl Till QuE TAM TAC DONG CUA TY GIA THUC LEN cAN cAN THUONG MAI VIETNAM • • Chuyen nganh : Tai chinh - Ngan hang Ma s6: 60340201 LU~N VAN T~C Si KINH TE NGUCH HVONG DAN KHOA HQC: PGS.TS NGUYEN NGQC f>TNH TP.HB Chi Minh - Nam 2013 LOI CAM BOAN Toi xin co la i cam doan rfulg day h\ cong trinh nghien ctru cua toi v6i S\I giup cua thAy hu6ng dftn la PGS.TS NguySn NgQc Dinh; sf> l i~u th6ng ke la trung th\fc va n{) i dung, k~t qua nghien Clru CUa lu~n van chua tirng duqc cong b6 bfit CU cong trinh m\o cho t6i thai di~m hi~n TP HCM, 22 th{mg 11 nam 2013 ~ Thi Mai Qu~ Tam MVCLVC Trang Trang ph\1 bia Liri cam doan M\)C I\)C Danh ffi\)C cac hinh ve, d6 thj ModAu Chtrf>i tuQTlg nghien Clru 1.4 Phuong phap nghien cl.ru J cau J b' '~ cuu ' 1.5 K et at ng h ten ChtrO'ng 2: TAng quan nghien cu11 • •• • • • • •• •• •.• • 2.1 Co so Ly thuy€t 1.1 Ty gia hf>i doai 2.1.1.1 Phan lo~i t:Y gia hf>i doai 2.1.1.2 Cac nha.n t6 fu1h hu6ng d€n tY gia 2.1.1.3 Vai tro va tac d()ng cua tY gia 14 2.1.2 Can can thuong m~i 17 2.1.3 Hi~n tm;mg duang cong chu J va m()t s6 nhan t6 tac d()ng Len cac can thuong mc;ti theo ly thuy€t duang cong J 18 2.1.4 H~ s6 co gian xu~t nh~p khAu va di8u ki~n M arshall -Lerner 18 2.2 Cac nghien cl.ru tht,.rc nghi~m lien quan 19 Chtr«ng 3: Thii t ki nghien cll'u 23 3.1 Xiy d\fllg mOblob nghien cU~u 23 1.1 Cac mo hinh va ki~m dinh Slr dl,lllg lu~ van 23 3.1.1.1 Ki~m dinh nghi~m dan vi (Unit roots test) .24 1.1.2 Xac dinh d9 trS t6i da, t6i uu 25 3.1.1.3 Ki~m tra tl,l' tuang quem ctia ph§n du 26 1.1.4 Ki~m dinh tinh 6n dinh mo hinh Var v6i d9 trS duqc chQn 26 3.1.1.5 Ki~m djnh d6ng lien k~t theo phuang phap Johansen 26 3.1.1.6 Mo hinh hi~u chinh sai s6 ECM 27 3.1 1.7 KiSm djnh nhan qua Granger .27 3.1.1.8 Phan tich ham phtm ung dgy 28 3.1.1.9 Phan tich phuong sai phan 28 3.1.2 Thu th~p va tinh toan sA li~u 28 3.2 Cac btr&c tii o hanh ki~m djnh 29 Chuang 4: Thao lu~n k~t qua nghien ctl'u 30 4.1 Ki~m dinh mfii quan h~ gifi'a can din thtr«ng m~i va ty gh\ th\fC da phU'O'o g (REER) 30 4.1.1 Trtrimg hQl> Vi~t Nam -Hoa kf .30 4.1.1.1 Ki~m djnh tinh dirng 30 4.1.1.2 Xac djnh d(> tr~ t6i da va t6i uu ctia mo hinh 31 4.1 1.3 K i€m djnh tl,l' tuang quan phfin du 32 4.1.1.4 Ki~m dinh tinh 6n dinh ctia mo hinh 33 4.1 1.5 K~t qua ki~m dinh mo hinh Var 33 4.1.1.6 Ki~m dinh granger 34 4.1.1.7 Ham phan ung 35 4.1.1.8 Phan tich phan phuong sai 35 4.1.2 T rtrimg hQ'P Vi~t Nam - Nh ~ t ban 36 4.1.2.1 Ki~m dinh tinh dirng (ki6m dinh nghi~m don vi) 36 ' I 4.1.2.2 Xac djnh tre- toi da va toi uu cua mo hinh 37 1.2.3 Kiem djnh tv tuong quan ph~n du 38 4.1.2.4 Ki~m djnh tinh An djnh cua mo hinh .38 4.1.2.5 K€t qua mo hinh Var .39 4.1 2.6 Ki8m dinh Granger Var 39 4.1.2.7 Ki6m dinh ham thuc dAy 40 4.1.2.8 Phan tich phuong sai phan 40 4.1.3 T ruimg hQ'P Vi~t Nam - Nhom nuO'c quan h~ thuon g m~i IO'n cua Vi ~t Nam 41 4.1.3.1 Ki8m djnh tinh dirng (kiSm dinh nghi~m don vi) 42 , , , J 4.1.3.2 Xac dinh tre tOi da va toi uu .43 4.1.3.3 Ki~m dinh tv tuong quan ph~n du 44 4.1.3.4 Ki~m djnh tinh An dinh cua mo hinh 44 4.1.3.5 K~t qua hAi quy mo hinh Var 44 4.1.3.6 Ki~m djnh Granger Var .45 4.1.3.7 Ki6m dinh ham thuc dAy 45 4.1.3.8 Phan tich phan phuong sai .46 4.2 Ki~m d jnb m6i quan h~ gifra Ca n can thuong m~i va cy gia th'!C SOng phtrong (RE R) Vi~t Nam - Han Qu6c .46 4.2.1 Ki~m djnh tinh dirng (ki~m djnh nghi~m dan vi) 46 4.2.2 X'ac dinh ; da va' tor : uu cua rno~ hlnh ' 47 d~9 tre~ t01 J d'nh K 1em tl,r tuang quan ph).an du 48 4.2.4 Ki~m djnh tinh 6n djnh cua mo hinh 49 4.2.5 Ki~m dinh dBng lien k~t 49 4.2.6 K~t qua mo hinh VECM .50 4.2.7 Ki~m dinh Granger 51 4.2.8 Ki~m dinh ham thll'c dfry (IRF) 51 Chtrong 5: K~t lu~n va ki~n nghj 52 5.1 Tom tit k~t qua nghien cll'u 52 5.2 H~n ch~ ctia nghien cll'u 53 5.3 D~ xu At tir k~t qua nghien ctl'u 54 Tai li~u tham khao Ph1,111}C 01: Thu nh~p tb1}'C ctia Vi~t Nam va cac ntrcYC d6i tac tir nam 1996 d~n nam 2012, nam g6c 2005 Ph1,1c l1,1c 02: Ty gia danh nghia cac ntr6'c so v&i Vi~t Nam tir nam 1996 d~n nam 2012 Ph1,111}C 03: Chi s6 ghi tieu dung Vi~t Nam va cac DU'cYC tir nam 1996 d~n nam 2012, nam g6c 2005 Ph1,1l1,1c 04: Ty gia th1}'c cac nuo·c d6i v&i Vi~t Nam tir n am 1996 d~n nam 2012, nam g6c 2005 Ph1,1l1,1c 05: K~t qua ki~m djnh tinh dimg cac bi~n mo hinh Ph1,1l1,1c 06: K~t qua mo hinh Var gifra Vi~t Nam- Hoa ky, Nb~t Ban va nh6m ntr6'c Pht,~ lt,~c 07: K~t qua ki~m djnh mo hinh hi~u chinh sai sA VECM gifra Vi~t Nam - Han QuAe Pht,~c lt,~c 08: Bang tAng hQl> xuit nh~p khAu Vi~t Nam dAi v6i cac dBi tac Pht,~ lt,~c 09: Bing tinh GDP th\fC cua Vi~t Na m tit nam 1996 d~n nam 2012 DANH Ml)C CAC HiNH vE, DO THJ Trang Hinh 2.1 : Can can thuang m~i va 1~ phat t1r nam 2002 - 2010 17 - duemg cong J 18 thi bieu dien ' }, Hinh 2.2 : Do TnrO'ng hQ'P ki~m djnh anh hU'img ctia tY gia thvc da phtrong len can can tbu0) indicating that depreciation leads to improve the trade balance for Algeria However in short-run we expect w1to be negative (w10 and ro3 0 - 6>, LnY,- LnY' il- - 6> LnREER.-d and IV measures the adjustment speed between the short-nm and long-nm disequilibrium Then we perform the Granger-Causality test in order to examine the short-run relations among the four variables used in balance of trade regression equation in other word this test is done to see the short run causality running from independent variable, to the dependent variable To solve this problem, we utilize the technique developed by Granger (1969) and improve later by Sims {1972) The relationship between those variables can be captured by a VARmodeL Then, if we want to test whether X, causes Y, , we analyze that how much of the present Y, can be illustrated by lagged values ofY, and X, In the Granger causality we test null hypothesis that X, does not granger causeY,; and if we can reject the null hypothesis, it means that X, does Granger cause Y, So the bivariate regression form for the Granger causation is written as follows: Y, = ~ +ti-l A,Y, ;+ L11-l jl;X ,; +u, (3) Published by Sciedu Press 105 /SSN 1923-4023 E-ISSN 1923-4031 www.sciedu.ca/ijfr Internationa l Journal of Financial Research Vol 3, No 4; 2012 Finally the dynamic behavior of our model can be analyzed using: fRF to show if there is evidence of the J-curve hypothesis in all cases of study Empirical Results and Interpretations 5.1 Unit Root Test The results over the period are reported in table- t (see appendi~) According to ADF test, PP test all variables are not stationary at level We djfferenced the data and run our test again, as a result the hypothesis of nonstationarity or presence of unil root is rejected at 99 percent level of confidence; for all variables, so all variables are integrated in order 1(1) in all qases The multiple individual time-series variables are found to be integrated of order one Where TB is the total trade balance of Algeria, and LTB 111.us LTB 11.frnn present the Algeria bilateral trade balance with US and France respectively Y is the GDP of Algeria, Yw Is the world GDP, Y fl'll present the GDP of France, and Yus is the US GDP 5.2 Cointegration Test & Error Correction Model According to the table-2 in appendix, Both the ''trace statistic" and "eigenvalue test" lead to the rejection of the null hypothesis of none coitegrating vectors against the altemativ~ hypothesis of (one or more cointcgrating vectors) at 5% level of significance for all model (Alg-US, Alg-Fra and Alg-world) Table-3 in appendix presents the results of long run cointegfating vector coefficient of the trade balance model Based on the estimated cointegrating vector and after nonnahzing our variables by the trade balance, the long-run equilibrium equation can be written as: Algeria-France case: LnTBt = -32.7 1604 +0 199907LnREERt- 0.579529LnYt + I0.79883LnYt*-0.07Trend (0.10160) (0.216 18) (2.93385) Where Yt* is the GDP of France, TBt is the Algeria bilateral trade balance with France Algeria-US case: LnTBt = -40.36075+2.218097LnREERt - 0.59549LnYt +6.15612LnYt*- 0.15Trend (0.60298) (0.29892) (2.48363) Where Yt* is the GDP of US, TBt is the Algeria bilateral trade balance with US Algeria-world: LnTBt =-45.26725+1.707 153LnREERt- 149773LnYt +3.1?2719LnYt*- 0.06Trend (0.49867) (0.56988) (0.64720) Where Yt• is the World GDP, TBt is the Algeria's total trade balance with all its major trading partners (rest of world) In our study according to the equations above the real domes ic income has significant and negative impact on the trade balance for all models (Algeria-US, Algeria-France and Algeria-world), this due to when national incomes rise, the residents demand a great amounts of goods and services because Algeria's is very dependent on its import abroad, and imports is composed especially in consumption goods (foqd materials, transport equipment, medical product, ) and capital goods, so any augmentation on the domestic income lead to more imports leading to the deterioration in trade balance Foreign income has significant positive impact on the trade balance for all cases this due to the rise in foreign income increase the demand for Algeria's exports because both US and France are industrialized countries so when their income rise the demand for output will be increased this ~ads to increase their production, this in tum rise their demand for Algeria's exports (especially raw materials, energy( According to equations, in the long-run the real depreciation has a significant positive impact on bilateral trade balance of Algeria with respect to US and France, and also RfER shows a significant positive impact on Algeria's total trade balance These results due to the fact that in the long run the volume effect dominate the value effect, which lead to improve trade balance so, devaluation will imprdve the trade balance in the long-run, this mean that the Marshall-Lemer condition is held in long-run for all cases of our study Published by Sciedu Press 106 ISSN 1923-4023 E-ISSN 1923-4031 www.sciedu.ca/ijfr International Journal of Financial Research Vol 3, No 4; 2012 5.3 Error Correction Model (ECM) Algeria-US t.Ln TBt = -0.2524 -0.3177 t.Ln TBt -I -0.8983 t.Ln REERt-2 -4.2925 t.Ln Yt-1 + 12.951 t.Ln Y•t-1-0.2359 ECTt-1 Algeria-France t.Ln TBt =-0.0998 -0.0965 t.Ln TBt -1-0.9254 t.Ln REERt-2 -2.2261 t.Ln Yt-1 +2.2261 t.Ln Y*t-2 -0.214EC Tt-l Algeria-world t.Ln TBt = -0.0577 -0.2343 t.Ln TBt -2 -0.6409 t.Ln REERt-2 -0.1988 t.Ln Yt-2+0.242 t.Ln Y•t-2 -0.381 EC Tt-l According to the table-4 in appendix and the equations above, the short-run dynamic estimate suggests that two-period lag REER shows a negative and significant impact on trade balance for all cases, which indicate that the REER depreciation has significant negative impact on trade balance in short-run The trade balance deteriorates initially afier depreciation and some time along the way it starts to improve until it reaches its long-run equilibrium; this time lag comes about as an impact of several Jags such as recognition, decision, delivery, replacement and production following a real depreciation The estimate suggests also that the one-period lag domestic income has significant and negative impact on the bilateral trade balance of Algeria with respect to France and US, means that the change in domestic income in the previous period has an effect on temporary trade balance, for Algeria-world case we see that the two-period lag domestic income has a negative and significant impact on temporary trade balance The two-period lag foreign income (Y*) shows a significant and positive impact on the total trade balance and in the bilateral trade balance of Algeria with France; the change in the US income in the previous period has a posihve and significant impact on the temporary bilateral trade balance of Algeria with respect to US We see also that the trade balance change in the previous period has a negative and significant effect on temporary trade balance in both cases Algeria with respect to France and US; in the case of Algeria-world we see that the two-lag period of trade balance has a negative and significant impact on the temporary trade balance The result displays coefficient of ECt- 1(speed of adjustment) has a correct sign (negative) meaningful and relatively higher (ECt-1) coefficient (-0.2359), (-0.214} for the case of Algeria with US and France respectively, and higher coefficient (-0.381) in the case of Algeria with world These signify that the adjustment process to an exogenous shock is rather higher In other words there is a rapid speed of adjustment back to the equilibrium This test suggests that real effective exchange rate, and all other variables have a significant impact on balance of trade in the short-run tor all cases of the study 5.4 Granger Causality Test (Result and Interpretation) Results report in the table-S in appendix, Granger Causality test shows that causality goes unidirectionally from Real Effective Exchange Rate (REER), foreign income (Y•), and domestic income (Y) to Trade Balance (TB) 5.5 Impulse Response Analysis According to the (figure I, II, Ill) in appendix J-curve effect is observed for Algeria in its bilateral trade with the US A real effective exchange rate shock initially worsened Algeria's real trade balance with respect to the US The deterioration lasts for quarters, after the trade balance improved before falling again to a value lower than the initial value Beyond quarters there is an improvement as the trade balance settles to a new long run equilibrium level which is largely higher than the initial value the improvement is about 9% A weak form of J-curve is observed for Algeria in its bilateral trade with France a J-curve effect is observed for Algeria in its total trade balance A real effective exchange rate shock initially worsened Algeria's real trade balance The deterioration lasts for quarters, after which the trade balance improved before falling again, after quarter it improved by about 1% then it slightly deteriorate until the 12 quarters Beyond 12 quarters the trade balance settles to a new long run equilibrium level which is higher than the initial value, the improvement is about 0.5% Conclusion In this paper we assess the long and short run effects of real effective exchange rate (REER) on the Algeria's trade balance (TB) and Algeria's bilateral trade balance with US and France in a long period from 1981 Q I to 2009Q4 The P11blished by Scied11 PresJ· 107 /SSN 1923-4023 E-ISSN 1923-4031 www.sciedu.ca/ijfr international Journal of Fipancial Research Vol 3, No.4; 2012 base model for each case includes the log of bilateral trade ratio (exports/imports), the log of real effective exchange rate, and the log of real domestic and foreign income The Johansen test confirms the presence of a long run co integrating relationship among the variables used for this study The study also reveals that the real effective exchange rate has a significant positive impact on Algeria's bilateral trade balance with US and France, and on Algeria's total trade balance in long-run, and it has significant negative impact on trade balance for all cases in the short-run We fi nd that in long-run the effect of REER in case of bilateral trade balance is more significant w1th the major export partner (US) than with major import partner (France) The Granger Causaljty test shows that causality goes unidirectionally fi'om Real Effective Exchange Rate (REER), foreign income (Y*), and domestic income (Y) to Trade Balance (TB); Granger causality test confirms the causal relation between exchange rate and bilateral trade balance wilh respect to US and France, it affirms also the causal relation between exchange rate and total balance of trade of Algeria; so the real effective exchange rate help in predicting the trade balance It can be conclude that real depreciation has a long-run positive impact on the total trade balance of Algeria, and on Algeria bilateral trade balance with respect to US, we find in both cases the evidence of J-curve hypothesis In the case of Algeria-France we observed a weak form of J-curve Overall, the results of the generalized impulse response analyses suggest that the Marshall-Lemer condition holds in the long run with varying degree of J-curve effects in the short run In all, we get the overall conclusion that the devaluation of Algeria's currency as a whole seems to be beneficial for Algeria's trade References Ahmed, N (2000) Export response to trade liberalization in Bangladesh: a cointegration analysis Applied Economics, 32, 1077-1084 http:l/dx.doi.org/10.1080/000368400322138 Asian Development Bank (ADB) (2006) Asian Development Outlook 2006 Update, p 47 Aziz, M N (2003, October) Devaluation: Impact on Bangladesh Economy The Cost and Management, September-October, 16-21 Bahmani-Oskooee, M (1995) Real and norrunal effective exchange rates for 22 LDCs: 1971: I - 1990:4 Applied Econometrics, 27, 591-604 http://dx.doi.org/l 0.1080/00036849500000048 Bhattarai, D K., & Armah, M K (2005) The Effects of Exchange Rate on the Trade Balance in Ghana:Evidence from Cointegration Analysis Cottingham United Kingdom: Centre for Economic Policy Business School, University of Hull Cottingham David, M G., & Guadalupe, F A.-U (2006) Exchange Rate Policy and Trade Balance.A cointcgration analysis of the argentine experience since 1962 Munich Personal RePEc (\rchive Paper No 151 Dickey, D., & Fuller, W ( 1979) Distribution of the Estimators for Autoregressive Time Series with a Unit Root Journal ofthe America11 Statistical Association 74, 427-431 Fleming, J (1962) Domestic Financial Policies under Fixed and under Floating Exchange Rates IMF staff paper 9, (November) 369-379 Granger, C W (1969) Investigating causal relations by econometric models and cross-spectral methods Econometrica, 7, 424-438 http://dx.doi.org/l 0.2307/ 1912791 Gomes, F A., & Paz, L S (2005) Can real exchange rat~ devaluation improve trade balance? The 1990-1998 Brazilian case Applied Economics Letlers, 12, 525-528 http://dx.doi.org/l I 080/13504850500076908 Islam, M (2003) Exchange Rate Policy of Algeria - Not Floating Does Not Mean Sinking Keynote Paper presented at dialogue organized by Centrefor Policy Dialogue, Algeria- January 2, 2003 Johansen, S (1988) Statistical Analysis of Cointegrating Vectors Journal of Economic Dy namics and Control 23 I -54 http://dx.doi.org/l I 016/0 165-1889(88)90041-3 Juselius, K ( 1990) Maximum likelihood estimation and inference on cointcgration- with application to the demand for money Economics and Statistics, 52, 169-210 Oxford Bulletin of Statistics Mussa, M (2002) Exchange Rate Regimes in an Increasingly Integrated World Economy Washington DC: International Monetary Fund Occasional Paper 193 Published by Sciedu Press 108 ISSN 1923-4023 £-ISSN 1923-4031 www.sciedu.ca/ijfr Vol 3, No.4; 2012 International Journal of Financial Research Narayan, P K (2004) New Zealand's trade balance: evidence of the J-Curve and granger causality Applied Economics Leiters, I I, 351-354 http://dx doi.org/l I 08011350485042000228187 Nusrate, A (2008) The Role of Exchange Rate in Trade Balance: Empirics from Bangladesh University of Birmingham, UK Onafowora (2003) Exchange rate and trade balance in East Asia: is therea J-curve? Economics Br1lletin, 5(18), 1-13 Phillips-Perron {1988) Testing for a Unit Root in Time Series Regression Biometrika, 75, 335-346 http://dx.doi.org/l 0.1 093/biomet/75.2.335 Rose, A K (1991) The role of exchange rate in a popular model of international trade: Does the Marshall-Lemer condition hold? Journal ofInternational Economics 30, 301-316 http://dx.doi.org/l 0.10 16/0022-1996(9 1)90024-Z Singh, T (2002) India's trade balance: the role of income and exchange rates Journal of Policy Modeling, 24, 437-452 http://dx.doi.org/1 0.10 16/S0 161-8938(02)00 124-2 Tavlas, S (2003) The Economics of Exchange Rate Regimes: A Review Essay Oxford: Blackwell Publishing Ltd Tsen, W H (2006) Is there a long-run relationship between trade balance and terms of trade? The case of Malaysia Applied Economics Letters 13, 307-3 I I http://dx.doi.org/l 0.1080113504850500393428 Wickham, P ( 1985) The Choice of Exchange Rate Regime in Developing Countries JMF Staff Papers, 32(2), 248-28 http:/ldx.doi.org/10.2307/3866841 Notes Note I Null hypothesis for Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) is the series bas a unit root (non-stationary) The critical values for ADF are -3.65 (without trend), -4.26 (with trend) at 1%, -2.96 (without trend) -3.56 (with trend) at 5% and -2.62 (without trend), -3.21(with trend) at 10% level of significance The critical values for PP test are -2.58 (without trend),-4.04(with trend) at 1%, -1.94 (without trend) -3.44 (with trend) at 5% and -1.61 (without trend), -3.15 at I 0% level of significance which have been tabulated from Mackinnon ( 1996) one-sided p-values Note In table 4: • indicate the independent variable is significant a 10%, •• indicate the significant at5% and ••• refers to the significant a I o/o level Note n the table 5: the F-statistic values of overall significance are given in the table Number of observations is given in parenthesis • •• and ••• indicate the rejection of the null hypothesis that vertical variable does not cause the respective horizontal variable to change, or vertical variables cannot help in predicting the horizontal variables at 10%,5% and 1% level of significance respectively Vars: variables Table I Stationary test for the fundamentals: ADF and PP tests 1(0) 1(1) LTB L TBAia-US L TBAI,·Ffl LREER LY L Yus LYF LY, LTB 6L TBAia·US 6L TB ,1.r., 6LREER 6LY 6L Yus 6LYF, 6LYw Published by Sciedu Press Augmen ted Dickey Fuller test With trend No trend -4.428665··· -0.940462 -3.794207** -0.850395 -4.484012* -3.205412 -0.928760 -1.577769 -0.877311 1.384541 -1.124652 3.711937 -1.994499 5.956951 -2.436961 5.038352 -6.063546* -6.112802··· -10 17161 - 10.2!633··· -12.029 10 -12.08018··· -8.834675* -8.695275 -4.700837* -3.69617••• -6.933178••• -3.75123··· -9.987645··· -3.80 151*** -4.868520··· -3.895745••• 109 P hillips-Pe rro n test With trend -4.120594••• -3.614667 -4.707657••• -1.437822 -1.0 15446 10.35295 -2.096971 -1.958671 -13.39662··· -15.69605··· -12.27236"' -8.982639"' -5.298544••• -6.384985••• -9.969901••• -10.66563 No trend -2.036515 -0.963024 -3.318178••• -1.352273 4.850152 -0.757483 8.034538 4.520608 -13.50371••• -15.20809 -12.21715 -8.900972••• -3.522788··· -1.994892••• -5.027776 -9.872073••• JSSN 1923-4023 E-ISSN 1923-4031 www.sciedu.cafijfr International Journal of Financial Research Vol 3, No 4; 2012 Table Johansen's cointegration test (Sample: 1981 Q 1- 2009Q4) Cases Null hypothesis Alternative Trace te~t Hypothesis Statistics 5% Critical value Alg/US r=O r.:!1 r.::2 Alg!Fra r.::3 r=O Alglworld r.:!1 r.:!2 r.:!3 r =0 r.:!1 r.:!2 r.:!3 Maximal Eigenvalue test Statistics 5% Critical value r=l r=2 r=3 r=4 65.1098,5* 31.62926 14.77697 1.257139 55.24578 35.01090 18.3977 3.841466 33.48059* 16.85229 13.51983 1.257232 30.18507 24.25202 17.14769 3.841466 r=l r=2 r=3 r=4 r=1 r=2 r=3 59.34656* 36.19700* 16.00909 3.648443 75.97823* 35.42540 11 00450 4.070277 55.24578 35.01090 18.3977 3.841 466 33.81507* 20.18791 12.06065 3.239845 30.18507 24.25202 17.14769 3.841466 63.87610 42.91525 25.8721 I 40.55284* 24.02090 6.934270 12.51798 4.070227 32.11 832 25.82321 19.38702 12.51798 r=4 Table Estimated coefficients derived by normalizing on lnTB Country LnTB Alg!Fra 1.00000 Alg!US Alg!World Published by Sciedu Press 1.00000 1.00000 1nREER 1nY lnY* c Trend -32.71604 -0.078041 199907 -0.579529 10.79883 (0.10160) (0.2 16 18) (2.93385) [ 1.96755] [-2.68077] (3.68077] 2.218097 -0.59549 156112 (2.48363) (0.60298) (0.29892) (3.67854] [-1.99210] [2.47867] 1.707153 -1.14977~ 3.102719 (0.49867) (0.56988~ [3.42342] [-2.01757] 110 (0.04827) [-1.61687] -40.36075 -0 150745 -45.26725 -0.063814 (0.64720) (0.02082) [4.79405) [-3.04643) /SSN 1923-4023 E-ISSN 1923-4031 www.sciedu.ca/ijfr International Journal of Financial Research Vol 3, No.4; 2012 Table Estimated ECM Model Independent variables Coefficient Standard error Algeria-US C(Constant) ALn TBt-1 Ln REERt-2 6Ln Yt-1 6Ln Y*t-1 EC Tt-l -0.2524••• -0.3177••• -0.8983··· -4.2925* 12.951** -0.2359*** 0.095 0.112 0.235 2.263 6.593 0.086 C(Constant) -0.0998* 0.059 6Ln TBt-1 -0.0965**• 0.038 -0.9254*** 6Ln REERt-2 0.145 6Ln Yt-1 -2.2261*** 825 6Ln Y*t-1 2.226** 0.841 ECTt-1 -0.214*** 0.051 Algeria-World C(Constant) -0.0577** 0.025 0.2343*** 6Ln TBt-2 0.083 6Ln REERt-2 -0.6409··· 0.248 6Ln Y*t-2 0.4210* 0.251 6Ln Yt-2 -0.1988* 0.108 EC Tt-l -0.38!••• 0.068 Table Paitwise Granger Causality test (F-statistic; sample: 1981Ql-2009Q4; lags: 2) Algeria-F ranee ~ 6LnTB ALnREER s Alge-US Alg-Francc Alg-world - 6LnTB 6LnREER LnY 6LnY• 4.4055** 1.92473* 2.87588• 0.68688 6LnTB ALnREER 6LnY 6LnY* 2.98754** 3.06388** 2.68210** (113) {113} (113) 6LnTB ALnREER 6LnY 6LnY• 2.92384* 4.00692** 2.87979* (113) (113) (113) - ALnY• ALnY {113) - 1.50036 {113) 1.00218 (1 13) 0.83293 (113) 1.50036 0.92521 (113) (113) - 0.62749 (114) (114) (114) (1 14) 1.50036 1.05055 - - (114) (114) 0.95369 0.48132 (113) (113) 2.39783 {113) 0.41648 0.48132 (11 3) (113) - - 1.10388 1.39006 0.58572 0.69853 2.34674 0.55348 (113) (1 13) {113) 0.17603 3.64525 0.36994 (104) (114) (114) 2.2676 (113) 0.05283 0.48132 (114) (114) - {113) (113) (113) 2.93279* ( 114) - ·' - 15 - II 196e ~ ~ ~~ ~ Figure I Trading partners Share in Algeria's exports, annually basis (1981 -2009) Published by Sciedu Press Ill /SSN 1923-4023 E-ISSN 1913-4031 www.sciedu.ca/ijfr International Journa l of Financial Research Vol 3, No.4; 2012 - I 15 I - l ,_ i ~ l li ~ ,990 ,_ :200C) lL i J I~ ~purtm