Hạn chế đầu tiên của bài nghiên cứu này là về mẫu dữ liệu quốc gia. Các quốc gia trong bài nghiên cứu này chỉ là các quốc gia đang phát triển ở châu Á, số liệu không quá lớn khi nghiên cứu đa quốc gia. Do đó, hướng nghiên cứu tiếp theo là có thể mở rộng mẫu ra cho các quốc gia toàn châu Á, sau đó, lại chia theo các khu vực địa lý nhằm phát hiện các bằng chứng thực nghiệm chi tiết hơn nữa.
Hạn chế thứ hai trong bài nghiên cứu này là phương pháp hồi quy. Tuy bài nghiên cứu có sử dụng phương pháp khá tối ưu là GMM hệ thống, nhưng hiện nay, đã có các phương pháp nghiên cứu hiện đại hơn có khả năng phân tích chính xác số liệu hồi quy và thậm chí phân tích mối quan hệ nhân quả qua lại. Vì đa phần các biến trong kinh tế vĩ mô đều không có tác động một chiều từ biến này sang biến khác mà luôn có tác động hai chiều. Do đó, hướng nghiên cứu tiếp theo có thể sử dụng các phương pháp hồi quy nâng cao hơn hiện nay như: 3SLS…
DANH MỤC TÀI LIỆU THAM KHẢO Danh mục tài liệu tiếng Việt
1. Trần Ngọc Thơ và các đồng nghiệp, 2012. Giáo trình tài chính quốc tế. Đại học Kinh tế Thành phố Hồ Chí Minh.
2. Nguyễn Tiến Dũng, 2015. Tác động bất cân xứng của các cú sốc thị trường dầu thô đến tỷ giá hối đoái của Việt Nam. Luận văn Thạc sỹ. Đại học Kinh tế Thành phố Hồ Chí Minh.
3. Nguyễn Thị Lan Hương, 2015. Giả thuyết thâm hụt kép: Mối quan hệ giữa thâm hụt ngân sách và thâm hụt tài khoản vãng lai thông qua phân tích dữ liệu bảng ở các nước Đông Nam Á. Luận văn Thạc sỹ. Đại học Kinh tế Thành phố Hồ Chí Minh.
Danh mục tài liệu tiếng Anh
1. Maurice Obstfeld and Kenneth Rogoff, 1994. The International approach to the current account. Cambridge: NBER Working Papers.
2. Paul R. Bergin and Steven M. Sheffrin, 2000. Interest Rates, Exchange Rates and Present Value Models of the Current Account. Oxford:The Economic Journal.
3. James M. Nason a , John H. Rogers, 2006. The present-value model of the current account has been rejected: Round up the usual suspects. Washington: Journal of International Economics 68 (2006) 159 – 187.
4. Lucun Yang, 2011. An Empirical Analysis of Current Account Determinants in Emerging Asian Economies. Cardiff : Cardiff Economics Working Papers.
5. Paul Krugman, 2009. How Did Economists Get It So Wrong. New york: The New York Times Magazine.
6. Christopher J. Erceg, Luca Guerrieri, and Christopher Gust, 2005. Expansionary Fiscal Shocks and the Trade Deficit. International Finance Discussion Papers. 7. Marianne Baxter, 1995. International Trade and Business Cycles. Cambridge:
8. Bernheim, B. Douglas, 1988. Budget deficits and the balance of trade. Cambridge: Tax Policy and the Economy vol. 2.
9. Blanchard, Olivier J., Galí, Jordi, 2010. The macroeconomic effects of oil price shocks: why are the 2000s so different from the 1970s? National Bureau of Economic Research, Inc, pp. 373–421.
10.Blinder, Alan S., Rudd, Jeremy B., 2008. The supply shock explanation of the great stagflation revisited. Working Papers 1097.
11.Bussiere, Matthieu, Fratzscher, Marcel, Muller, Gernot J., 2004. Current account dynamics in OECD and EU acceding countries — an intertemporal approach. European Central Bank Working Paper Series No. 311, February.
12.Chinn, Menzie D., Ito, Hiro, 2007. Current account balances, financial development, and institutions: assaying the world ‘savings glut’. Journal of International Money Finance. 26, 546–569.
13.Chinn, Menzie D., Prasad, Eswar S., 2003. Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration. Journal of International Economics. 59, 47–76.
14.Choi, I., 2001. Unit root tests for panel data. Journal of International Money Finance. 20, 249–272.
15.Debelle, Guy, Faruqee, Hamid, 1996. What determines the current account? A crosss - ectional and panel approach. IMF Working Paper.
16.Glick, Reuven, Rogoff, Kenneth, 1995. Global versus country-specific productivity shocks and the current account. Journal of International Economics. 35, 159–192. 17.Gruber, J.W., Kamin, S.B., 2007. Explaining the global pattern of current account
imbalances. J. Int. Money Fina Journal of International Money Finance. 26, 500– 522.
18.Gruber, Joseph, Kamin, Steven, 2009. Do differences in financial development explain the global pattern of current account imbalances? International Economic Review. 17 (4), 667–688.
19.Hamilton, James D., 2003. What is an oil shock? Journal of International Economics. 113 (2), 363–398.
20.Hamilton, James D., 2009. Causes and consequences of the oil shock of 2007–08. Brookings Papers on Economic Activity, pp. 215–259.
21.Higgins, Matthew, 1998. Demography, national savings, and international capital flows. International Economic Review. 39 (2), 343–369.
22.Jones, Clifton T., 1990. OPEC behavior under falling prices: implications for cartel stability. Journal of Energy.11 (3), 117–129.
23.Kilian, Lutz, 2008. Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. International Economic Review. 99 (3), 1053–1069.
24.Kilian, Lutz, Rebucci, Alessandro, Spatafora, Nikola, 2009. Oil shocks and external balances. Journal of International Economics. 77 (2), 181–194.
25.Le, Thai-Ha, Chang, Youngho, 2013. Oil price shocks and trade imbalances. (adsbygoogle = window.adsbygoogle || []).push({});
Journal of Energy.36, 78–96.
26.Mann, Catherine L., 2002. Perspectives on the U.S. current account deficit and sustainability. Journal of Economic Perspectives. 16 (3), 131–152.
27.Medlock, Kenneth B., Jaffe, Amy Myers, Hartley, Peter R., 2011. Shale gas and U.S. national security. James A. Baker III Institute for Public Policy.
28.Morse, Edward L., Lee, Eric G., Ahn, Daniel P., Doshi, Aakash, Kleinman, Seth M., Yuen, Anthony, 2012. Energy 2020: North America, the New Middle East?
Citi Global Perspectives & Solutions (GPS).
29.Pesaran, M. Hashem, 2007. A simple panel unit root test in the presence of cross- section dependence. Journal of Applied Economics. 22 (2), 265–312.
30.Huntington, Hillard G., 2015. Crude oil trade and current account deficits. Energy Economics.
PHỤ LỤC Bảng 4.1. Thống kê mô tả
Bảng 4.2. Ma trận tương quan giai đoạn toàn mẫu Bảng 4.1. Thống kê mô tả saving 26.24472 17.49366 -236.428 25.539 64.717 513 netexportoil .013147 .0456659 -.099941 -.0015135 .137162 520 tradeopen 96.63154 73.88192 15.239 80.7455 439.657 520 gov -1.347052 11.55817 -151.309 -1.688 43.303 520 incomeper 8.297248 1.420669 4.58529 8.38388 10.9333 520 age 56.66571 14.42136 27.7955 56.01515 98.0511 520 ca 2.740821 17.19508 -242.188 .423 60.271 520 variable mean sd min p50 max N
0.0000 0.0000 0.0293 0.0000 0.0000 0.5063 saving 0.6187 -0.3384 0.0962 0.7020 0.2037 0.0294 1.0000 0.0000 0.7729 0.0000 0.0000 0.0000 netexportoil 0.3282 0.0127 0.2482 0.2628 -0.2187 1.0000 0.0000 0.0000 0.0000 0.0017 tradeopen 0.1913 -0.3990 0.4694 0.1371 1.0000 0.0000 0.0000 0.0000 gov 0.7116 -0.2216 0.2081 1.0000 0.0000 0.0000 incomeper 0.3544 -0.5491 1.0000 0.0000 age -0.3221 1.0000 ca 1.0000 ca age income~r gov tradeo~n netexp~l saving
Bảng 4.3. Ma trận tương quan giai đoạn trước và sau khủng hoảng.
Bảng 4.3. Ma trận tương quan giai đoạn trước và sau khủng hoảng.
26.24472 17.49366 -236.428 25.539 64.717 513 .013147 .0456659 -.099941 -.0015135 .137162 520 96.63154 73.88192 15.239 80.7455 439.657 520 -1.347052 11.55817 -151.309 -1.688 43.303 520 8.297248 1.420669 4.58529 8.38388 10.9333 520 56.66571 14.42136 27.7955 56.01515 98.0511 520 Total 2.740821 17.19508 -242.188 .423 60.271 520 25.08576 18.66456 -236.428 23.971 64.717 380 .0137902 .0453929 -.099941 -.001302 .137162 380 95.42837 74.89068 15.239 79.59255 439.657 380 -1.528492 12.45712 -151.309 -1.4545 43.303 380 8.058495 1.413037 4.58529 8.11822 10.6649 380 60.23036 13.86698 31.2821 60.32765 98.0511 380 1 2.180032 18.52555 -242.188 0 60.271 380 29.55604 13.11 -3.683 30.28 58.667 133 .0114011 .0465182 -.088338 -.0022645 .128366 140 99.89731 71.23137 28.0524 85.5235 377.302 140 -.8545714 8.680678 -20.392 -2.667 34.001 140 8.945292 1.230028 6.59083 8.962915 10.9333 140 46.9902 11.09978 27.7955 46.87745 69.3146 140 0 4.262964 12.84607 -26.867 2.6065 45.22 140 dumtime mean sd min p50 max N
0.0000 0.0000 0.6142 0.0000 0.0001 0.4649 saving 0.5908 -0.2728 0.0259 0.7208 0.1956 -0.0376 1.0000 0.0000 0.1668 0.0000 0.0003 0.0000 netexportoil 0.2874 0.0711 0.2455 0.1854 -0.2423 1.0000 0.0009 0.0000 0.0000 0.0084 tradeopen 0.1692 -0.4369 0.4877 0.1350 1.0000 0.0000 0.0001 0.0028 gov 0.6963 -0.2022 0.1527 1.0000 0.0000 0.0000 incomeper 0.3193 -0.4903 1.0000 0.0000 age -0.3082 1.0000 ca 1.0000 ca age income~r gov tradeo~n netexp~l saving
Bảng 4.4. Kết quả ước lượng với xuất khẩu dầu thô thuần OLS FEM 0.0000 0.0000 0.0008 0.0000 0.0055 0.0001 saving 0.8148 -0.5731 0.2878 0.6140 0.2397 0.3396 1.0000 0.0000 0.0087 0.0001 0.0000 0.0744 netexportoil 0.5139 -0.2209 0.3277 0.5777 -0.1513 1.0000 0.0007 0.0000 0.0000 0.0806 tradeopen 0.2845 -0.3875 0.4601 0.1482 1.0000 0.0000 0.0000 0.0000 gov 0.7972 -0.3748 0.4675 1.0000 0.0000 0.0000 incomeper 0.5173 -0.5251 1.0000 0.0000 age -0.4351 1.0000 ca 1.0000 ca age income~r gov tradeo~n netexp~l saving
_cons 59.81594 17.17063 3.48 0.001 26.08253 93.54935 netexportoil 52.20751 12.69817 4.11 0.000 27.26071 77.15432 tradeopen .005201 .0084294 0.62 0.538 -.0113594 .0217613 gov .8809185 .0468561 18.80 0.000 .788865 .9729719 incomeper2 .9609012 .2579279 3.73 0.000 .4541762 1.467626 incomeper -14.20754 4.186815 -3.39 0.001 -22.43296 -5.982132 age -.1285107 .0423145 -3.04 0.003 -.2116417 -.0453797 ca Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 153453.215 519 295.670934 Root MSE = 11.166 Adj R-squared = 0.5783 Residual 63961.8244 513 124.681919 R-squared = 0.5832 Model 89491.3905 6 14915.2317 Prob > F = 0.0000 F( 6, 513) = 119.63 Source SS df MS Number of obs = 520
REM _cons -23.64832 21.88877 -1.08 0.280 -66.65488 19.35825 netexportoil 120.1752 42.66438 2.82 0.005 36.34923 204.0013 tradeopen -.0207129 .0238196 -0.87 0.385 -.0675131 .0260874 gov .8933471 .0465576 19.19 0.000 .8018718 .9848223 incomeper2 -.4554446 .3136427 -1.45 0.147 -1.071683 .1607936 incomeper 8.177064 5.071348 1.61 0.108 -1.787007 18.14114 age -.1334282 .061315 -2.18 0.030 -.2538986 -.0129579 ca Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.1558 Prob > F = 0.0000 F(6,494) = 113.76 overall = 0.5029 max = 26 between = 0.3926 avg = 26.0 R-sq: within = 0.5801 Obs per group: min = 26 Group variable: id Number of groups = 20 Fixed-effects (within) regression Number of obs = 520
_cons -11.50011 20.72972 -0.55 0.579 -52.12962 29.12939 netexportoil 86.1253 29.5982 2.91 0.004 28.1139 144.1367 tradeopen -.0003712 .0178656 -0.02 0.983 -.0353871 .0346447 gov .9070858 .0432385 20.98 0.000 .8223399 .9918316 incomeper2 -.2220946 .2983229 -0.74 0.457 -.8067967 .3626075 incomeper 4.489275 4.84094 0.93 0.354 -4.998793 13.97734 age -.1260882 .0557249 -2.26 0.024 -.2353069 -.0168694 ca Coef. Std. Err. z P>|z| [95% Conf. Interval] corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Wald chi2(6) = 698.27 overall = 0.5541 max = 26 between = 0.5124 avg = 26.0 R-sq: within = 0.5785 Obs per group: min = 26 Group variable: id Number of groups = 20 Random-effects GLS regression Number of obs = 520
Hausman Test
Bảng 4.5. Kết quả ước lượng với xuất khẩu và nhập khẩu OLS
Prob>chi2 = 0.3244 = 6.96
chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg netexportoil 120.1752 86.1253 34.04994 30.72777 tradeopen -.0207129 -.0003712 -.0203417 .0157542 gov .8933471 .9070858 -.0137387 .0172639 incomeper2 -.4554446 -.2220946 -.23335 .0968255 incomeper 8.177064 4.489275 3.687789 1.511248 age -.1334282 -.1260882 -.0073401 .0255787 fe re Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients _cons 59.71903 17.18871 3.47 0.001 25.94995 93.4881 oilneg 41.80461 36.16251 1.16 0.248 -29.24055 112.8498 oilpos 55.86863 17.42166 3.21 0.001 21.6419 90.09537 tradeopen .0048552 .0085115 0.57 0.569 -.0118666 .021577 gov .8790473 .0472913 18.59 0.000 .7861384 .9719562 incomeper2 .9533774 .2593149 3.68 0.000 .4439251 1.46283 incomeper -14.14614 4.195281 -3.37 0.001 -22.38822 -5.904054 age -.1288519 .0423664 -3.04 0.002 -.2120853 -.0456184 ca Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 153453.215 519 295.670934 Root MSE = 11.176 Adj R-squared = 0.5776 Residual 63950.0481 512 124.902438 R-squared = 0.5833 Model 89503.1668 7 12786.1667 Prob > F = 0.0000 F( 7, 512) = 102.37 Source SS df MS Number of obs = 520
FEM REM _cons -26.53466 21.91783 -1.21 0.227 -69.59853 16.52922 oilneg -3.150452 85.32751 -0.04 0.971 -170.8009 164.5 oilpos 163.6033 49.91688 3.28 0.001 65.52728 261.6794 tradeopen -.0356031 .0253975 -1.40 0.162 -.0855039 .0142976 gov .8688512 .0487394 17.83 0.000 .7730887 .9646136 incomeper2 -.4945516 .3139554 -1.58 0.116 -1.111407 .122304 incomeper 8.786869 5.075413 1.73 0.084 -1.185239 18.75898 age -.1338024 .0612051 -2.19 0.029 -.2540575 -.0135473 ca Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.2239 Prob > F = 0.0000 F(7,493) = 98.26 overall = 0.4891 max = 26 between = 0.3726 avg = 26.0 R-sq: within = 0.5825 Obs per group: min = 26 Group variable: id Number of groups = 20 Fixed-effects (within) regression Number of obs = 520
_cons -13.16233 20.7769 -0.63 0.526 -53.88431 27.55966 oilneg -.2316243 71.78936 -0.00 0.997 -140.9362 140.4729 oilpos 114.7288 36.316 3.16 0.002 43.55073 185.9068 tradeopen -.0093208 .0191217 -0.49 0.626 -.0467987 .0281571 gov .8921894 .0446129 20.00 0.000 .8047497 .979629 incomeper2 -.2653816 .2993963 -0.89 0.375 -.8521875 .3214243 incomeper 5.021282 4.850503 1.04 0.301 -4.485529 14.52809 age -.1299406 .0559865 -2.32 0.020 -.2396721 -.0202091 ca Coef. Std. Err. z P>|z| [95% Conf. Interval] corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Wald chi2(7) = 701.64 overall = 0.5489 max = 26 between = 0.4968 avg = 26.0 R-sq: within = 0.5806 Obs per group: min = 26 Group variable: id Number of groups = 20 Random-effects GLS regression Number of obs = 520
Hausman Test
Prob>chi2 = 0.2748 = 7.53
chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg oilneg -3.150452 -.2316243 -2.918828 46.12019 oilpos 163.6033 114.7288 48.87456 34.2468 tradeopen -.0356031 -.0093208 -.0262823 .0167151 gov .8688512 .8921894 -.0233382 .0196269 incomeper2 -.4945516 -.2653816 -.22917 .0944979 incomeper 8.786869 5.021282 3.765587 1.494134 age -.1338024 -.1299406 -.0038619 .0247302 fe re Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients
Bảng 4.6. Kết quả kiệm định có biến giả tương tác của Việt Nam và xuất khẩu dầu thô thô thuần (adsbygoogle = window.adsbygoogle || []).push({});
Toàn thời gian
(Robust, but weakened by many instruments.)
Hansen test of overid. restrictions: chi2(17) = 13.74 Prob > chi2 = 0.686 (Not robust, but not weakened by many instruments.)
Sargan test of overid. restrictions: chi2(17) = 2.09 Prob > chi2 = 1.000 Arellano-Bond test for AR(2) in first differences: z = -1.02 Pr > z = 0.306 Arellano-Bond test for AR(1) in first differences: z = -0.89 Pr > z = 0.372 DL(1/24).netexportoil collapsed
GMM-type (missing=0, separate instruments for each period unless collapsed) _cons
Standard
Instruments for levels equation
Warning: Uncorrected two-step standard errors are unreliable.
_cons -125.3599 11.41754 -10.98 0.000 -147.7378 -102.9819 dumvnoil -4979.189 844.9012 -5.89 0.000 -6635.165 -3323.213 netexportoil 97.3378 4.729353 20.58 0.000 88.06844 106.6072 tradeopen .0317533 .011831 2.68 0.007 .008565 .0549416 gov .4344311 .0118527 36.65 0.000 .4112003 .457662 incomeper2 -.1033389 .2356094 -0.44 0.661 -.5651249 .3584471 incomeper 11.69559 3.315493 3.53 0.000 5.197345 18.19384 age .6991849 .06883 10.16 0.000 .5642806 .8340892 ca Coef. Std. Err. z P>|z| [95% Conf. Interval] Prob > chi2 = 0.000 max = 26 Wald chi2(7) = 6587.60 avg = 26.00 Number of instruments = 25 Obs per group: min = 26 Time variable : time Number of groups = 20 Group variable: id Number of obs = 520
Trước khủng hoảng
(Robust, but weakened by many instruments.)
Hansen test of overid. restrictions: chi2(10) = 12.34 Prob > chi2 = 0.263 (Not robust, but not weakened by many instruments.)
Sargan test of overid. restrictions: chi2(10) = 1.00 Prob > chi2 = 1.000 Arellano-Bond test for AR(2) in first differences: z = -1.49 Pr > z = 0.136 Arellano-Bond test for AR(1) in first differences: z = -0.99 Pr > z = 0.322 DL(1/24).netexportoil collapsed
GMM-type (missing=0, separate instruments for each period unless collapsed) _cons
Standard
Instruments for levels equation
Warning: Uncorrected two-step standard errors are unreliable.
_cons -4.61117 47.32957 -0.10 0.922 -97.37542 88.15308 dumvnoil -866.6433 443.4933 -1.95 0.051 -1735.874 2.587569 netexportoil 143.7637 4.397099 32.70 0.000 135.1455 152.3818 tradeopen .0193942 .0087032 2.23 0.026 .0023363 .0364522 gov .2225865 .0190962 11.66 0.000 .1851587 .2600144 incomeper2 .3768885 .6796279 0.55 0.579 -.9551576 1.708935 incomeper -1.279679 11.58017 -0.11 0.912 -23.9764 21.41705 age -.1463493 .0638781 -2.29 0.022 -.2715481 -.0211505 ca Coef. Std. Err. z P>|z| [95% Conf. Interval] Prob > chi2 = 0.000 max = 19 Wald chi2(7) = 10491.98 avg = 19.00 Number of instruments = 18 Obs per group: min = 19 Time variable : time Number of groups = 20 Group variable: id Number of obs = 380
Sau khủng hoảng
(Robust, but weakened by many instruments.)
Hansen test of overid. restrictions: chi2(17) = 13.89 Prob > chi2 = 0.675 (Not robust, but not weakened by many instruments.)
Sargan test of overid. restrictions: chi2(17) = 17.82 Prob > chi2 = 0.400 Arellano-Bond test for AR(2) in first differences: z = -1.10 Pr > z = 0.270 Arellano-Bond test for AR(1) in first differences: z = -1.48 Pr > z = 0.139 DL(1/24).netexportoil collapsed
GMM-type (missing=0, separate instruments for each period unless collapsed) _cons
Standard
Instruments for levels equation
Warning: Uncorrected two-step standard errors are unreliable.
_cons 104.681 131.3649 0.80 0.426 -152.7894 362.1514 dumvnoil -113.9427 519.9069 -0.22 0.827 -1132.941 905.0561 netexportoil 62.46096 7.795158 8.01 0.000 47.18273 77.73919 tradeopen .0233166 .0144328 1.62 0.106 -.0049711 .0516044