Determinants of current account in vietnam an intertemporal approach (2)

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM THE NETHERLANDS INSTITUTE OF SOCIAL STUDIES THE HAGUE VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF CURRENT ACCOUNT IN VIETNAM: AN INTERTEMPORAL APPROACH BY DO NGUYEN KHANH LINH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, DECEMBER 2013 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAMTHE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF CURRENT ACCOUNT IN VIETNAM: AN INTERTEMPORAL APPROACH A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS BY DO NGUYEN KHANH LINH Academic Supervisor: NGUYEN VAN NGAI HO CHI MINH CITY, DECEMBER 2013 Acknowledgement I would like to express sincere gratitude to my supervisor – Prof Nguyen Van Ngai for scientific guidance, patient encouragement and useful advices, which he has provided throughout the time of preparation and accomplishment of this paper I also would like to thank to Dr Truong Dang Thuy who gave precious comments on the draft Special thanks to Prof Nguyen Trong Hoai, and Dr Pham Khanh Nam for guidance and support as our program administrators I also would like to give my deepest thanks to my parents, and my beloved husband Thanh Binh for his brilliant advices, spiritual encouragements i supports, and Table of Contents Acknowledgement i Table of Contents ii List of tables iv List of figures iv Abbreviations v Abstract vi CHAPTER 1: INTRODUCTION 1.1 Problem Statement 1.2 Research objectives and questions 1.3 Justification of the thesis 1.4 Organization of the research CHAPTER 2: LITERATURE REVIEW 2.1 Introduction 2.2 Definition of Current Account (CA) 2.3 Theoretical Literature Review 2.3.1 The old approaches 2.3.2 The intertemporal approach 2.3.3 Determinants of CA balance based on the intertemporal approach .23 2.4 Empirical researches 27 2.5 Conceptual framework 40 2.6 Chapter summary 41 CHAPTER 3: DATA AND RESEARCH METHODOLOGY 42 3.1 Data 42 3.2 Research Methodology 43 3.2.1 Analytical Framework 43 3.2.2 Model Specification 44 ii 3.2.2.2 Cointegration Test 45 CHAPTER 4: FINDINGS AND DISCUSSIONS 50 4.1 Descriptive statistics 50 4.2 The dynamics of variables 51 4.3 The correlation matrix 53 4.4 The regression result 54 4.4.1 Unit Root Test 54 4.4.2 ARDL Test 55 CHAPTER 5: CONCLUSION AND POLICY IMPLICATION 59 5.1 Conclusion 59 5.2 Policy implication 60 5.3 Limitations and directions for further studies 60 5.3.1 Limitation 60 5.3.2 Further studies 61 REFERENCES 62 APPENDICES 68 ii List of tables Table 2-1: Empirical studies about determinants of CA balance 33 Table 3-1: The measurements and expected signs of variables 43 Table 3-2: Critical values for Dickey Fuller test 45 Table 3-3: Critical values bounds for testing the hypothesis of no – cointegration relationship 47 Table 4-1: Descriptive statistics .50 Table 4-2: The correlation matrix of variables .53 Table 4-3: The values of stationary tests of all variables .54 Table 4-4: The summary of result 55 Table 4-5: Results of cointegration (m = 1) 56 Table 4-6: ARDL equations chosen with lowest AIC and SIC’s values 56 Table 4-7: Estimated long – run coefficients using the ARDL approach .57 List of figures Figure 1-1: Current account of Vietnam from 1999 to 2012 Figure 2-1: Temporary fall in income 15 Figure 2-2: Current account acts like a shock absorber 22 Figure 2-3: The conceptual framework 41 Figure 3-1: The Analytical framework 44 Figure 4-1: The dynamics of variables 52 Abbreviations ARDL: Autoregressive Distributed Lag CA : Current Account CPI : Consumer Price Index ECM : Error Correction Model GMM : Generalized Method of Moments GSO : General Statistics Office of Vietnam IFS : International Financial Statistics IMF : International Monetary Finance LDC : Least Developed Countries NFA : Net Foreign Assets OECD: Organization for Economic Co-operation and Development REER : Real Effective Exchange Rate USD : United States Dollar VND : Vietnam Dong Abstract This study attempts to explain theoretically and examine empirically influences of macroeconomics factors on the current account behavior in Vietnam from 2000Q1 to 2010Q2 The study employs the intertemporal approach to build the theory of Vietnam current account behavior’s determinants and applies Autoregressive Distributed Lag method to estimate the model Theoretically, intertemporal approach was an approach broadly employed to decide determinants of current account recently It treats current account as an absorber for the economy in facing a shock in income The study discovers that initial net foreign assets, trade openness, real effective exchange rate and inflation volatility have significant relationships with current account in Vietnam CHAPTER 1: INTRODUCTION This study attempts to explain theoretically and test empirically influences of macroeconomics factors on the current account behavior in Vietnam The study employs the intertemporal approach to build the theory of Vietnam current account behavior’s determinant and applies autoregressive distributed lag method to estimate the model Theoretically, intertemporal approach was an approach broadly employed to decide determinants of current account recently It treats current account as an absorber for the economy in facing a shock in income Determinants of current account will be determined based on this idea 1.1 Problem Statement The current account is a crucial indicator of a country’s economic performance (Knight and Scacciavillani 1998) Current account deficit could damage the economy in many ways First, current account deficit that accompanies by budget deficit leads to the increasing of debts (Krugman, 1987) According to Ministry of Finance, public debt reached 52.6% of GDP, foreign debt hit 38.8% of GDP in 2010 and was equal 56.6% compared to GDP in 2011 Second, in an economy that applies fixed exchange rate regime and has weak foreign reserve, a prolonged current account deficit may raise the probability of a monetary crisis (Edwards, 2002) The Asian financial crisis in 1997 is a good example for this situation Third, a serious current account deficit raises people’s expectation of depreciation that increases the volatility of the foreign exchange market Altogether, maintaining the balance of current account at suitable level is one of the majority missions of macroeconomics policymakers Consequently, strong understanding about the determinants of current account balance becomes an important subject that requires careful researches (Yang 2011) Like most developing countries in the world, Vietnam mostly suffers from the current account deficit over the past 12 years There are two deficit periods of current account: the low deficit period in 2002-2006 and the high deficit period in 2006-2010 The first deficit period started in 2002 and reached the lowest value (1.93 billion USD) in the year 2003 After 2003, it turned to improve until getting the balance in 2006 However, the situation became worse after 2006 It reached 6.953 billion USD in the year 2007 and -10.823 billion USD in the year 2008 Current account had improved and had an increasing tendency since 2008 It got the surplus position in the year 2012 (see Figure 1-1) Altogether, the situation of current account is still very unpredictable As observations mentioned in the previous paragraph, the erratic current account level could have a negative impact on the stability of Vietnamese macroeconomic, which has become instability for long time owing to the impact of global crisis and the products of many overlap policy Therefore, it is very essential to identify determinants of Vietnam’s current account balance so that the country could restore external balance by controlling those causal factors through suitable macroeconomic policy options Hence, the aim of this paper is to shed the light on finding the determinants of current account in intertemporal approach perspective Figure 1-1: Current account of Vietnam from 1999 to 2012 15000 15.00% 10000 5000 3.55% 6.66% 2.10% 0.19% -1.06% -0.27% -1.72% -2.11% -4.89% -9.77% -12.05% -5000 -10000 -15000 0.00% -4.14% -7.12% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 CA CA/GDP 10.00% 5.00% -5.00% -10.00% -15.00% ỵt ỵt ỵt ỵt d t u − ( c ƒ u t ) ( e c d t ≤ λ (ƒ ( ct ) e t ) e d t − ƒ ( c t d t ) ) e 0 = ỵt ỵ t ⇒ƒ u(ct )e dt ≤ ƒ u(ct )e dt 0 t Hence, path c1 is eed a ind maximum Appendix 5: Testing co – integration for the conventional ECM equation with “m = 1” by using Eviews 1) The conventional ECM equation with m = 1: ∆CAt = a1 + α2CAt–1 + α3M2t–1 + α4 OPENt–1 + α5REERt–1 + α6VOLt–1 + α7INFAt–1 1 1 + ) bi ∆CAt–i + ) cj1 ∆M2(t–j1) + ) dj2 ∆OPEN(t–j2) + ) ej3 ∆REER(t–j3) j1=0 j2=0 i= j3=0 1 + ) fj4 ∆VOL(t–j4) + ) kj5 ∆INFA(t–j5) + ut 2) Eviews results: j4= j5=0 a The regression result: Dependent Variable: D(CA) Method: Least Squares Date: 10/03/13 Time: 10:05 Sample (adjusted): 2000Q4 2010Q2 Included observations: 39 after adjustments R-squared Variable Coefficient Std Error t-Statistic Prob C CA(-1) M2(-1) INFA(-1) OPEN(-1) REER(-1) VOL(-1) D(M2) D(INFA) D(OPEN) D(REER) D(VOL) D(CA(-1)) D(M2(-1)) D(INFA(-1)) D(OPEN(-1)) D(REER(-1)) D(VOL(-1)) 1.335211 -1.302488 -0.142079 -0.284233 -0.233777 -0.715434 0.054242 0.226263 0.208998 -0.334219 -0.573960 0.024728 0.075827 0.095537 0.647009 0.090911 -0.164359 -0.000129 0.384656 0.263999 0.046003 0.073689 0.089845 0.247272 0.012769 0.087545 0.161641 0.060919 0.350206 0.005618 0.155820 0.098890 0.235028 0.078166 0.349472 0.006685 3.471185 -4.933690 -3.088497 -3.857204 -2.602004 -2.893314 4.247820 2.584527 1.292972 -5.486271 -1.638920 4.401617 0.486633 0.966094 2.752907 1.163047 -0.470307 -0.019293 0.0023 0.0001 0.0056 0.0009 0.0166 0.0087 0.0004 0.0173 0.2101 0.0000 0.1161 0.0002 0.6316 0.3450 0.0119 0.2579 0.6430 0.9848 0.923321 Mean dependent var -0.001777 Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.861248 0.033418 0.023453 89.27993 14.87468 0.000000 S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat 0.089715 -3.655381 -2.887584 -3.379902 1.811848 b Testing serial correlation for the conventional ECM Breusch-Godfrey Serial Correlation LM Test: F-statistic Obs*R-squared 0.896931 3.364484 Prob F(2,19) Prob Chi-Square(2) 0.4244 0.1860 Test Equation: Dependent Variable: RESID Method: Least Squares Date: 01/05/14 Time: 08:30 Sample: 2000Q4 2010Q2 Included observations: 39 Presample missing value lagged residuals set to zero Variable Coefficient Std Error t-Statistic Prob C CA(-1) M2(-1) INFA(-1) OPEN(-1) REER(-1) VOL(-1) D(M2) D(INFA) D(OPEN) D(REER) D(VOL) D(CA(-1)) D(M2(-1)) D(INFA(-1)) D(OPEN(-1)) D(REER(-1)) D(VOL(-1)) RESID(-1) RESID(-2) -0.073317 -0.067772 0.004853 -0.021675 0.017191 0.050149 0.005049 0.030466 0.002892 0.021831 0.088813 8.17E-05 -0.065196 0.020715 -0.004343 -0.031963 -0.035112 -0.002434 0.274069 -0.324177 0.390780 0.413091 0.046410 0.102124 0.091200 0.252366 0.016134 0.096139 0.177624 0.072561 0.375881 0.006014 0.208979 0.146770 0.290266 0.094180 0.374170 0.009740 0.390039 0.294101 -0.187618 -0.164062 0.104570 -0.212244 0.188500 0.198716 0.312915 0.316893 0.016283 0.300863 0.236281 0.013579 -0.311976 0.141139 -0.014961 -0.339379 -0.093841 -0.249875 0.702671 -1.102263 0.8532 0.8714 0.9178 0.8342 0.8525 0.8446 0.7578 0.7548 0.9872 0.7668 0.8157 0.9893 0.7585 0.8892 0.9882 0.7380 0.9262 0.8054 0.4908 0.2841 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.086269 -0.827462 0.033584 0.021429 91.03920 0.094414 0.999998 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat -2.03E-16 0.024843 -3.643036 -2.789927 -3.336948 2.125218 c The Wald test for C(2) = C(3) = C(4) = C(5) = C(6) = C(7) = Wald Test: Equation: M2_1 Test Statistic Value df 6.678482 40.07089 F-statistic Chi-square Probability (6, 21) 0.0005 0.0000 Value Std Err Null Hypothesis Summary: Normalized Restriction (= 0) C(2) C(3) C(4) C(5) C(6) C(7) -1.302488 -0.142079 -0.284233 -0.233777 -0.715434 0.054242 0.263999 0.046003 0.073689 0.089845 0.247272 0.012769 Restrictions are linear in coefficients Appendix 6: Testing co – integration for the conventional ECM equation with “m = 2” by using Eviews 1) The conventional ECM equation with m = 2: ∆CAt = a1 + α2CAt–1 + α3M2t–1 + α4OPENt–1 + α5REERt–1 + α6VOLt–1 + α7INFAt–1 2 2 + ) bi ∆CAt–i + ) cj1 ∆M2(t–j1) + ) dj2 ∆OPEN(t–j2) + ) ej3 ∆REER(t–j3) i= j1= j2= 2 j3=0 + ) fj4 ∆VOL(t–j4) + ) kj5 ∆INFA(t–j5) + ut j4=0 j5=0 2) Eviews results: a The regression result: Dependent Variable: D(CA) Method: Least Squares Date: 10/03/13 Time: 10:09 Sample (adjusted): 2001Q1 2010Q2 Included observations: 38 after adjustments Variable Coefficient Std Error t-Statistic Prob C CA(-1) M2(-1) INFA(-1) OPEN(-1) REER(-1) VOL(-1) D(M2) D(INFA) D(OPEN) D(REER) D(VOL) D(CA(-1)) D(M2(-1)) D(INFA(-1)) D(OPEN(-1)) D(REER(-1)) D(VOL(-1)) D(CA(-2)) D(M2(-2)) D(INFA(-2)) D(OPEN(-2)) D(REER(-2)) D(VOL(-2)) 1.064866 -1.027090 -0.090554 -0.377878 -0.219614 -0.538824 0.073327 0.285546 0.070094 -0.327861 -0.905617 0.032249 0.004942 0.102440 0.797450 0.170665 -0.622803 -0.011759 -0.144564 -0.141835 0.068359 0.017779 -0.351085 -0.009824 0.824249 0.412965 0.090476 0.118522 0.164962 0.519862 0.020720 0.122471 0.241276 0.084345 0.376857 0.007205 0.269511 0.114858 0.248933 0.102902 0.573452 0.013531 0.165032 0.116089 0.299076 0.104902 0.449367 0.007618 1.291924 -2.487114 -1.000860 -3.188262 -1.331297 -1.036475 3.538954 2.331546 0.290516 -3.887155 -2.403080 4.475713 0.018335 0.891883 3.203470 1.658522 -1.086059 -0.869036 -0.875978 -1.221778 0.228567 0.169481 -0.781287 -1.289562 0.2173 0.0261 0.3339 0.0066 0.2044 0.3176 0.0033 0.0352 0.7757 0.0016 0.0307 0.0005 0.9856 0.3875 0.0064 0.1194 0.2958 0.3995 0.3958 0.2420 0.8225 0.8678 0.4476 0.2181 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.956874 0.886023 0.030640 0.013143 97.49955 13.50552 0.000005 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat -0.002637 0.090757 -3.868397 -2.834133 -3.500414 2.390226 b Testing serial correlation for the conventional ECM Breusch-Godfrey Serial Correlation LM Test: F-statistic Obs*R-squared 2.874758 12.30916 Prob F(2,12) Prob Chi-Square(2) 0.0955 0.0021 Test Equation: Dependent Variable: RESID Method: Least Squares Date: 01/06/14 Time: 21:06 Sample: 2001Q1 2010Q2 Included observations: 38 Presample missing value lagged residuals set to zero Variable Coefficient Std Error t-Statistic Prob C CA(-1) M2(-1) INFA(-1) OPEN(-1) REER(-1) VOL(-1) D(M2) D(INFA) D(OPEN) D(REER) D(VOL) D(CA(-1)) D(M2(-1)) D(INFA(-1)) D(OPEN(-1)) D(REER(-1)) D(VOL(-1)) D(CA(-2)) D(M2(-2)) D(INFA(-2)) D(OPEN(-2)) D(REER(-2)) D(VOL(-2)) RESID(-1) RESID(-2) -1.284073 1.104705 0.132737 0.173048 0.249924 0.703131 -0.033859 0.010936 -0.130540 0.045541 0.173372 0.002565 -0.386570 -0.192804 -0.319146 0.016663 -0.206761 0.022833 -0.093310 -0.125904 -0.568448 -0.047975 0.019589 0.002840 -1.049318 -0.574166 0.960460 0.592998 0.104809 0.127699 0.186628 0.582408 0.023891 0.109026 0.224753 0.081439 0.347178 0.006508 0.303982 0.135237 0.261277 0.093954 0.516542 0.015343 0.153665 0.117068 0.362041 0.101252 0.404286 0.007172 0.488817 0.359229 -1.336935 1.862915 1.266464 1.355128 1.339155 1.207282 -1.417215 0.100308 -0.580815 0.559196 0.499376 0.394165 -1.271687 -1.425670 -1.221483 0.177352 -0.400279 1.488148 -0.607230 -1.075477 -1.570118 -0.473819 0.048453 0.395990 -2.146648 -1.598330 0.2060 0.0871 0.2294 0.2003 0.2053 0.2506 0.1819 0.9218 0.5721 0.5863 0.6265 0.7004 0.2276 0.1795 0.2454 0.8622 0.6960 0.1625 0.5550 0.3033 0.1424 0.6441 0.9622 0.6991 0.0530 0.1360 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.323925 -1.084564 0.027212 0.008886 104.9371 0.229981 0.999058 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat -1.09E-16 0.018847 -4.154586 -3.034132 -3.755937 2.130587 Appendix 7: Testing co – integration for the conventional ECM equation with “m = 3” by using Eviews 1) The conventional ECM equation with m = 3: ∆CAt = a1 + α2CAt–1 + α3M2t–1 + α4OPENt–1 + α5REERt–1 + α6VOLt–1 + α7INFAt–1 3 3 + ) bi ∆CAt–i + ) cj1 ∆M2(t–j1) + ) dj2 ∆OPEN(t–j2) + ) ej3 ∆REER(t–j3) i= j1= j2= 3 j3=0 + ) fj4 ∆VOL(t–j4) + ) kj5 ∆INFA(t–j5) + ut j4=0 j5=0 2) Eviews results: a The regression result: Dependent Variable: D(CA) Method: Least Squares Date: 10/04/13 Time: 20:21 Sample (adjusted): 2001Q2 2010Q2 Included observations: 37 after adjustments Variable Coefficient Std Error t-Statistic Prob C CA(-1) M2(-1) INFA(-1) OPEN(-1) REER(-1) VOL(-1) D(M2) D(INFA) D(OPEN) D(REER) D(VOL) D(CA(-1)) D(M2(-1)) D(INFA(-1)) D(OPEN(-1)) D(REER(-1)) D(VOL(-1)) D(CA(-2)) D(M2(-2)) D(INFA(-2)) D(OPEN(-2)) D(REER(-2)) D(VOL(-2)) D(CA(-3)) D(M2(-3)) D(INFA(-3)) D(OPEN(-3)) D(REER(-3)) D(VOL(-3)) 2.974000 -2.194169 -0.269241 -0.633426 -0.610846 -1.659997 0.152256 0.270325 0.202815 -0.229415 -0.909382 0.032997 0.813799 0.347228 1.290160 0.528983 0.067714 -0.073793 0.327573 -0.083890 0.487579 0.322052 -0.127808 -0.050311 0.174842 0.044983 0.741457 0.184370 0.304043 -0.021178 1.122103 0.498864 0.143039 0.155747 0.211958 0.742416 0.035346 0.150410 0.279276 0.151776 0.509250 0.010688 0.401294 0.132059 0.269074 0.216186 0.752038 0.026443 0.325518 0.157236 0.326747 0.129530 0.598070 0.017985 0.244705 0.126624 0.260261 0.133672 0.458177 0.007730 2.650379 -4.398333 -1.882295 -4.067023 -2.881915 -2.235940 4.307638 1.797252 0.726219 -1.511536 -1.785728 3.087250 2.027936 2.629339 4.794806 2.446885 0.090041 -2.790631 1.006313 -0.533532 1.492220 2.486316 -0.213700 -2.797386 0.714503 0.355245 2.848901 1.379275 0.663593 -2.739569 0.0329 0.0032 0.1018 0.0048 0.0236 0.0604 0.0035 0.1153 0.4913 0.1744 0.1173 0.0176 0.0822 0.0339 0.0020 0.0443 0.9308 0.0269 0.3478 0.6102 0.1793 0.0418 0.8369 0.0266 0.4980 0.7329 0.0247 0.2103 0.5282 0.0289 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.986374 0.929923 0.024356 0.004152 115.7564 17.47306 0.000339 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat -0.002761 0.092005 -4.635483 -3.329334 -4.175004 2.633255 b Testing serial correlation for the conventional ECM Breusch-Godfrey Serial Correlation LM Test: F-statistic Obs*R-squared 0.904628 9.831102 Prob F(2,5) Prob Chi-Square(2) 0.4620 0.0073 Test Equation: Dependent Variable: RESID Method: Least Squares Date: 01/06/14 Time: 21:11 Sample: 2001Q2 2010Q2 Included observations: 37 Presample missing value lagged residuals set to zero Variable Coefficient Std Error t-Statistic Prob C CA(-1) M2(-1) INFA(-1) OPEN(-1) REER(-1) VOL(-1) D(M2) D(INFA) D(OPEN) D(REER) D(VOL) D(CA(-1)) D(M2(-1)) D(INFA(-1)) D(OPEN(-1)) D(REER(-1)) D(VOL(-1)) D(CA(-2)) D(M2(-2)) D(INFA(-2)) D(OPEN(-2)) D(REER(-2)) D(VOL(-2)) D(CA(-3)) D(M2(-3)) D(INFA(-3)) D(OPEN(-3)) D(REER(-3)) D(VOL(-3)) RESID(-1) RESID(-2) 0.495302 0.268540 -0.104437 0.209371 0.008443 -0.428451 -0.014516 -0.037476 0.292097 0.062826 -0.436533 -0.003382 0.004527 -0.151900 -0.271058 0.061060 0.626759 0.001836 -0.100866 0.093027 -0.230373 -0.035850 0.448204 -0.005663 -0.053801 0.133647 -0.001329 -0.037238 0.279033 0.005386 -0.849857 0.159714 1.371338 0.559243 0.200118 0.245871 0.217346 0.956619 0.037569 0.169496 0.497253 0.197633 0.672297 0.012499 0.457570 0.196471 0.398872 0.234044 1.183536 0.029813 0.339108 0.210574 0.376293 0.134455 0.859808 0.023704 0.258403 0.195097 0.275242 0.143608 0.582044 0.008889 0.638740 0.756457 0.361181 0.480185 -0.521878 0.851551 0.038847 -0.447881 -0.386374 -0.221100 0.587423 0.317892 -0.649315 -0.270538 0.009894 -0.773139 -0.679563 0.260890 0.529564 0.061589 -0.297445 0.441781 -0.612218 -0.266629 0.521284 -0.238922 -0.208205 0.685029 -0.004827 -0.259301 0.479402 0.605915 -1.330522 0.211134 0.7327 0.6514 0.6240 0.4333 0.9705 0.6730 0.7151 0.8338 0.5824 0.7634 0.5448 0.7976 0.9925 0.4744 0.5270 0.8046 0.6191 0.9533 0.7781 0.6771 0.5672 0.8004 0.6244 0.8207 0.8433 0.5238 0.9963 0.8057 0.6519 0.5710 0.2408 0.8411 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.265705 -4.286921 0.024695 0.003049 121.4701 0.058363 1.000000 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat 10 1.78E-15 0.010740 -4.836220 -3.442994 -4.345043 2.263002 Appendix 8: The regression based on SIC 1) Using Microfit: Autoregressive Distributed Lag Estimates ARDL(1,1,1,0,0,1) selected based on Schwarz Bayesian Criterion ******************************************************************* Dependent variable is CA 40 observations used for estimation from 2000Q3 to 2010Q2 ******************************************************************* Regressor Coefficient Standard Error T-Ratio[Prob] CA(-1) -0.43651 0.15858 -2.7526[.010] 0.24267 0.071014 3.4172[.002] -0.29935 0.071518 -4.1856[.000] 0.59035 0.13826 4.2698[.000] INFA(-1) -0.81933 0.15995 -5.1225[.000] OPEN -0.23704 0.053190 -4.4564[.000] REER -0.30092 0.16641 -1.8083[.081] VOL 0.016280 0.0054014 3.0140[.005] VOL(-1) 0.028318 0.0049093 5.7683[.000] M2 M2(-1) INFA C 0.74142 0.23657 3.1341[.004] ******************************************************************* R-Squared 79637 R-Bar-Squared 0.73528 S.E of Regression 0.042102 F-stat F( 9, 30) 13.0361[.000] Mean of Dependent Variable -0.037419 S.D of Dependent Variable 081830 Residual Sum of Squares 053178 Equation Log-likelihood Akaike Info Criterion 65.7022 Schwarz Bayesian Criterion 57.2578 DW-statistic 2.1125 Durbin's h-statistic 75.7022 *NONE* ******************************************************************* 2) Using Eviews: Dependent Variable: CA Method: Least Squares Date: 10/04/13 Time: 21:27 Sample (adjusted): 2000Q3 2010Q2 Included observations: 40 after adjustments Variable Coefficient Std Error t-Statistic Prob CA(-1) M2 M2(-1) INFA INFA(-1) OPEN REER VOL VOL(-1) C -0.436507 0.242671 -0.299346 0.590345 -0.819325 -0.237037 -0.300924 0.016280 0.028318 0.741421 0.158579 0.071014 0.071518 0.138261 0.159948 0.053190 0.166413 0.005401 0.004909 0.236565 -2.752611 3.417227 -4.185569 4.269793 -5.122461 -4.456402 -1.808293 3.014010 5.768327 3.134108 0.0099 0.0018 0.0002 0.0002 0.0000 0.0001 0.0806 0.0052 0.0000 0.0038 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.796369 0.735279 0.042102 0.053178 75.70224 13.03613 0.000000 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat -0.037419 0.081830 -3.285112 -2.862892 -3.132451 2.112506 Estimated Long Run Coefficients using ARDL Approach selected based on Schwarz Bayesian 3) The long runthe regression, estimatedARDL(1,1,1,0,0,1) by Microfit ******************************************************************* Dependent variable is C 40 observations used for estimation from 2000Q3 to 2010Q2 ******************************************************************* Regressor Coefficient Standard Error T-Ratio[Prob] M2 0.027467 -0.039453 -1.4364[.161] INFA -0.15940 0.038467 -4.1438[.000] OPEN -0.16501 0.035342 -4.6689[.000] REER -.20948 0.11923 -1.7570[.089] VOL 0.031046 0.0066485 4.6697[.000] C 0.51613 0.17057 3.0259[.005] ******************************************************************* Appendix 9: The regression based on AIC 1) Using Microfit: Autoregressive Distributed Lag Estimates ARDL(1,1,1,1,0,1) selected based on Akaike Information Criterion ******************************************************************* Dependent variable is CA 40 observations used for estimation from 2000Q3 to 2010Q2 ******************************************************************* Regressor Coefficient Standard Error T-Ratio[Prob] CA(-1) -.36675 16250 M2 0.28240 0.074635 3.7838[.001] -0.31822 071287 -4.4639[.000] 0.47893 0.15508 3.0883[.004] INFA(-1) -0.72879 0.16836 -4.3289[.000] OPEN -0.27241 0.057373 -4.7480[.000] 0.10422 0.070390 1.4806[.149] REER -0.15434 0.19088 -.80859[.425] VOL 0.018520 0.0055091 3.3618[.002] VOL(-1) 0.026450 0.0049773 5.3141[.000] M2(-1) INFA OPEN(-1) -2.2570[.032] C 0.49618 0.28506 1.7406[.092] ******************************************************************* RSquared 0.81068 R-Bar-Squared 0.74540 S.E of Regression 0.041290 F-stat.F(10, 29) 12.4180[.000] Mean of Dependent Variable -0.037419 S.D of Dependent Variable 0.081830 Residual Sum of Squares 0.049441 Equation Log-likelihood 77.1597 Akaike Info Criterion 66.1597 Schwarz Bayesian Criterion 56.8709 DW-statistic 2.1433 Durbin's h-statistic *NONE* ******************************************************************* 2) Using Eviews Dependent Variable: CA Method: Least Squares Date: 01/04/14 Time: 16:23 Sample (adjusted): 2000Q3 2010Q2 Included observations: 40 after adjustments Variable Coefficient Std Error t-Statistic Prob CA(-1) M2 M2(-1) INFA INFA(-1) OPEN OPEN(-1) REER VOL VOL(-1) C -0.366754 0.282403 -0.318220 0.478925 -0.728794 -0.272407 0.104221 -0.154344 0.018520 0.026450 0.496178 0.162498 0.074635 0.071287 0.155075 0.168357 0.057373 0.070390 0.190881 0.005509 0.004977 0.285060 -2.256975 3.783803 -4.463901 3.088336 -4.328863 -4.747955 1.480618 -0.808585 3.361809 5.314107 1.740606 0.0317 0.0007 0.0001 0.0044 0.0002 0.0001 0.1495 0.4253 0.0022 0.0000 0.0924 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.810680 0.745398 0.041290 0.049441 77.15970 12.41800 0.000000 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat -0.037419 0.081830 -3.307985 -2.843543 -3.140058 2.143314 Estimated Long Run Coefficients using ARDL Approach selected based on Akaike Informato 3) The long runthe regression, estimatedARDL(1,1,1,1,0,1) by Microfit ******************************************************************* Dependent variable is 40 observations used for estimation from 2000Q3 to 2010Q2 ******************************************************************* Regressor Coefficient Standard Error T-Ratio[Prob] M2 0.029583 -0.026206 -0.88585[.383] INFA -0.18282 0.043462 -4.2064[.000] OPEN -0.12305 0.046692 -2.6354[.013] REER -0.11293 0.13882 -0.81349[.423] VOL 0.032903 0.0070652 4.6570[.000] C 0.36303 0.20342 1.7847[.085] ******************************************************************* ... to decide determinants of current account recently It treats current account as an absorber for the economy in facing a shock in income Determinants of current account will be determined based... American current account deficit Chin and Ito (2008), try to describe the determinants of CA by following the study of Chinn and Prasad (2003) They examine the medium-term determinants of current account. .. concentrating on the recent events could fail to find the determinants of CA balance Instead, intertemporal approach try to decide the CA balance’s determinants based on investigating effect factors in