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BỘ GIÁO DỤC VÀ ĐÀO TẠO T G ĐẠI C I THÂM T T THÂM L Ậ T ỒC Í Ă I SĨ - Ă 2013 C BỘ GIÁO DỤC VÀ ĐÀO TẠO T G ĐẠI C I THÂM T T THÂM C T - 60340201 L Ậ Ă SĨ : PGS.TS S T ỒC Í I - Ă 2013 C L T TÁC GIẢ LUẬ VĂ D I I U L T Ả B Gá ọ T p Đ ồC ạ Đạ ọ T ề Đạ ọ p T Tầ C T Đạ ọ ạT p ồC ọ p T T ầ – PGS.TS Đ T k X T ọ T ỉ óp ý è ề p ơ! Tá D L Trang T L pụ ụ ụ D ụ D ụ D ụ L ý ữ ắ ẽ ị Ầ Lý ọ ề ụ p áp Đ pạ Ý ĩ ễ ề B ụ 1.1 Cá ý ề ụ ó ụ 1.1.1 Lý ề ụ 1.1.2 Lý ề ụ 1.1.3 Thâm hụt ó ó thâm hụt p ầ 1.2 Cá lý hụt ềm quan h thâ m hụt 1.2.1 Thâm hụt tài khóa tác ó thâm 10 ng n thâm hụ 10 1.2.2 Thâm hụ có m ó Thâm ụ không uan h 1.2.3 Cá 11 ụ ụ ó 13 1.3 Đá á ề ụ ó ụ 15 21 2.1 2.2 21 p áp 2.2.1 ị 2.2.2 ị 22 ỗ 22 C ữ 27 2.2.3 ị G ữ ệ 3.1 T p ữ 3.2 ị 3.3 ị 29 ế qả ự 31 ệ 31 37 ỉ ( Error Correction Mechanism - ECM) 41 3.4 49 ị G ế L 51 í 4.1 Cá p 51 4.2 H ý 4.3 ý 4.4 L p Ả ó 52 ề 53 54 55 Ý IMF Qỹ ề GDP Tổ TDGDP B thâm ụ / FDGDP B thâm ụ / LnEX B INF B FDI V ECM Mô h ADF ị PP ị p ODA V pá , Ữ Ắ pẩ / GDP ó / GDP á pá ầ p ỉ A D -Fuller test ĩ BẢ B 3.1 T B 3.2 B 3.3 ị , BỂ ữ 36 37 f p -Perron khơng có ễ2 B 38 3.4 ị f p- ó ễ2 B 39 3.5 ị f nh Phillips-Perron không xu ễ2 B 40 3.6 ị f p- ó ễ2 B 41 3.7 p C ó B 3.8 T C B 42 pầ óp 3.9 ạ ị 45 f p -Perron không xu ễ2 B 3.10 45 ị f p- ễ2 B 46 3.11 óp B ắ C 3.12 47 (robust) B 3.13 (robust) óp G 50 óp G 51 ÁC Ồ Đồ ị 3.1 Thâm ụ Ị P ổ C L Á 2012 32 Đồ ị 3.2 Thâm ụ ó ổi C Á 2012 32 Đồ ị 3.3 Lạ p 33 Đồ ị 3.4 T á ổ C Á 6 ổ C 2012 Á 33 2012 ụ ụ 1: ị TDGD ụ ụ 3: ị I ụ ụ 4: ị L X ụ ụ 5: ị TDGD ó ễ2 66 ụ ụ 6: ị DGD ó ễ2 67 ụ ụ : ị I ụ ụ : ị L X ó ụ ụ :K ị DTDGD ụ ụ 10: ị ễ2 ễ2 64 ễ2 ó DTDGD ụ ụ 11: 64 65 ễ2 67 ễ2 68 ó ó ễ2 ễ2 C 69 óp 69 ụ ụ 12 T 68 pầ C 70 ụ ụ 13: ị ụ ụ 14: ị ễ2 ó ễ2 70 71 ụ ụ 15 p ắ C ó 71 ụ ụ 16: Hồ óp Granger ụ ụ 17: Hồ cho DTDGD óp G DGD 71 72 Current Account Deficits: Cross-Country Comparisons, Empirical Economics, vol 24, pp 389-402 56 Kouassi, Mougoue, & Kymm, 2004, Causality tests of the relationship between the twin deficits, Empirical Economics 29: 503-525 57 Kumhof M., Laxton D., 2009, Fiscal Deficits and Current Account Deficits, International Monetary Fund Working Paper, No 09/237 58 Im, K.S., Pesaran, M.H and Shin,Y., 2003, Testing for unit roots in heterogeneous panels, Journal of Econometrics 115: 53–74 59 Leachman, L.L., Francis, B., 2002 Twin deficits: apparition or reality? Applied Economics Letters 34, 1121–1132 60 Levin, A., Lin C and Chu, C J, 2002 Uni t Root Test in Panel Data: Asymptoti c and Finite Sample Properties, Journal of Econometrics 108, 1-24 David, D., WW., Sense of Community: A Definition and Theory, 61 McMillin, 1986, The I p f L I Human, Louisiana State University, Department of economics working paper series 62 Martin B and Michel N., 2009, Do Tax Cuts Generate Twin Deficits? A MultiCountry Analysis, Working Paper 63 Martin, B., J., 1971, National Income and the Price Level, 2nd edition, New York: McGraw Hill 64 Miller, S.M., Russek, F.S., 1989 Are the twins deficits really related? Contemporary Policy Issues 8, 91–115 65 Mundell, R., 1963, Capital Mobility and Stabilization Policy under Fixed and Flexible Exchange Rates, Canadian Journal of Economics and Political Science, November 66 Newey, W K and Kenneth D W., 1987, A Simple Positive Semi-De¯nite, Heteroscedasticity and Autocorrelation Consistent Covariance Matrix", Econometrica, 50, 703-708 67 Normandin, M., 1998, Budget deficit persistence and the twin deficits hypothesis, Journal of International Economics, 49 (1999) 171–193 68 Onafowora, O., 2003, Exchange rate and trade balance in East Asia: Is there a Jcurve Economic Bulletin, Vol 5, No.18, pp 1-13 69 Papadogonas, T and Stournaras, Y., 2006, Twin Deficits and Financial Integration in EU Member-States, Journal of Policy Modeling, vol 28, no 5, pp 595-602 70 Patillo, C., Romer, D and Weil, D.N., 2002, External debt and growth, IMF Working Paper, No 02/69 71 Patillo, C., Romer, D and Weil, D.N., 2004, What are the channels through which external debt affects growth?, IMF Working Paper, No 04/15 72 Pedroni, P., 1995), Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis, Indiana University Working Paper in Economics No 95-013 73 Piersanti, G., 2000, Current Account Dynamics and Expected Future Budget Deficits: Some International Evidence, Journal of International Money and Finance,vol 19, pp 255-171 74 Phillips, P.C.B., Perron, P., 1988, Testing for a unit root in time series regression, Biometrika, 75, 335–345 75 Reisen, H., 1998, Domestic Causes of Currency Crises: Policy Lessons for Crisis Avoidance, OECD Development CentreWorking Papers 136, OECD Publishing 76 Robel B., Massimo G., and Franc K., 2007, The Effects of Public Spending Shocks on Trade Balances and Budget Deficits in the European Union, University of Amsterdam 77 Rosensweig, J A and Tallman, E W, 1993, Fiscal Policy and Trade Adjustment: Are the Deficits Really Twins?, Economic Inquiry, 580-594 78 Salm S., 2003, The Budget Deficit and Economic Performance: A survey, Economic working paper series, No 03-12, University of Wollongong 79 .T 2002 I f Journal of Policy Modeling, 24, 437-452 80 Siddiqui A., 2007, India and South Asia Economic Developments in The Age of Globalization, M.E Sharpe, New York 81 Summers, L.H., 1988, Tax Policy and International Competitiveness, in J Frenkel(ed.) 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Robustness Checks and Robustness Tests in Applied Economics, Department of Economics University of California, San Diego 90 Yellen, J.L, 1989, Symposium on the Budget deficit, Journal of Economic Perspective Ụ LỤC ụ ụ 1: ị TDGD ễ2 Fisher-type unit-root test for TDGDP Based on Phillips-Perron tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = 25.00 AR parameter: Panel-specific Panel means: Included Time trend: Not included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) P Inverse normal Z Inverse logit t(34) L* Modified inv chi-squared Pm p-value 12.7965 0.0342 0.3494 0.1626 0.3840 0.5136 0.6355 0.4354 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ụ ụ 2: ị FDGDP, không x Fisher-type unit-root test for FDGDP Based on Phillips-Perron tests Ho: All panels contain unit roots Number of panels = Ha: At least one panel is Avg number of periods = stationary 25.00 AR parameter: Panel-specific Panel means: Included Time trend: Not included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) Inverse normal Inverse logit t(34) Modified inv chi-squared P Z L* Pm 37.6884 -4.0241 -4.1957 5.2436 p-value 0.0002 0.0000 0.0001 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ễ2 ụ ụ 3: ị I ễ2 Fisher-type unit-root test for INF Based on Phillips-Perron tests Ho: All panels contain unit roots Number of panels = Ha: At least one panel is Avg number of periods = stationary 25.00 AR parameter: Panel-specific Panel means: Included Time trend: Not included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) P Inverse normal Z Inverse logit t(34) L* Modified inv chi-squared Pm p-value 51.0344 -5.0763 -5.7525 7.9679 0.0000 0.0000 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ụ ụ 4: ị L X Fisher-type unit-root test for lnEX Based on Phillips-Perron tests Ho: All panels contain unit roots Number of panels = Ha: At least one panel is Avg number of periods = stationary 25.00 AR parameter: Panel-specific Panel means: Included Time trend: Not included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) P Inverse normal Z Inverse logit t(19) L* Modified inv chi-squared Pm 2.1612 1.8145 2.0883 -2.0083 p-value 0.9991 0.9652 0.9748 0.9777 ễ2 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ụ ụ 5: ị TDGD ó ễ2 Fisher-type unit-root test for TDGDP Based on Phillips-Perron tests Ho: All panels contain unit roots Number of panels = Ha: At least one panel is Avg number of periods = stationary 25.00 AR parameter: Panel-specific Panel means: Included Time trend: Included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) P Inverse normal Z Inverse logit t(34) L* Modified inv chi-squared Pm p-value 6.2880 1.1834 1.1883 -1.1659 0.9009 0.8817 0.8785 0.8782 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ụ lụ 6: ị DGD ó Fisher-type unit-root test for FDGDP Based on Phillips-Perron tests Ho: All panels contain unit roots Number of panels = Ha: At least one panel is Avg number of periods = stationary 25.00 AR parameter: Panel-specific Panel means: Included Time trend: Included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) Inverse normal Inverse logit t(34) Modified inv chi-squared P Z L* Pm 28.3215 -2.9231 -2.9559 3.3316 p-value 0.0050 0.0017 0.0028 0.0004 P statistic requires number of panels to be finite ễ2 Other statistics are suitable for finite or infinite number of panels ụ ụ : ị I ó ễ2 Fisher-type unit-root test for INF Based on Phillips-Perron tests Ho: All panels contain unit roots Number of panels = Ha: At least one panel is Avg number of periods = stationary 25.00 AR parameter: Panel-specific Panel means: Included Time trend: Included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) P Inverse normal Z Inverse logit t(34) L* Modified inv chi-squared Pm p-value 45.3166 -4.5929 -5.0518 6.8007 0.0000 0.0000 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ụ ụ : ị L X ó Fisher-type unit-root test for lnEX Based on Phillips-Perron tests Ho: All panels contain unit roots Number of panels = Ha: At least one panel is Avg number of periods = stationary 25.00 AR parameter: Panel-specific Panel means: Included Time trend: Included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) Inverse normal Inverse logit t(19) P Z L* Modified inv chi-squared Pm p-value 72.4241 -3.0262 -10.1457 0.0000 0.0012 0.0000 12.3340 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ễ2 ụ ụ : ị DTDGD ó ễ2 Fisher-type unit-root test for DTDGDP Based on Phillips-Perron tests Ho: All panels contain unit roots Number of panels = Ha: At least one panel is Avg number of periods = stationary 23.83 AR parameter: Panel-specific Panel means: Included Time trend: Not included Newey-West lags: lags Inverse chi-squared(12) Inverse normal Inverse logit t(34) Modified inv chi-squared P Z L* Pm Asymptotics: T -> Infinity Statistic p-value 101.5481 -8.2819 -11.5720 18.2789 0.0000 0.0000 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ụ ụ 10: ị DTDGD ó Fisher-type unit-root test for DTDGDP Based on Phillips-Perron tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = 23.83 AR parameter: Panel-specific Panel means: Included Time trend: Included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) Inverse normal Inverse logit t(34) Modified inv chi-squared P Z L* Pm 90.6994 -7.2642 -10.2371 16.0644 p-value 0.0000 0.0000 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ễ2 ụ ụ 11: C óp Fixed-effects (within) regression Group variable: Id Number of obs = Number of groups = 150 R-sq: within = 0.3016 between = 0.0019 overall = 0.0883 Obs per group: = avg = max = 17 25.0 27 corr(u_i, Xb) = -0.2136 F(3,5) Prob > F = = 10.65 0.0130 (Std Err adjusted for clusters in Id) Robust Coef Std Err TDGD t P>|t| P FDGD -1.329812 2550843 -5.21 0.003 INF 0633838 0638189 0.99 0.366 lnEX -2.35e-06 0000527 -0.04 0.966 _cons -.9047929 8118107 -1.11 0.316 [95% Conf Interval] -1.985528 -.1006679 -.0001379 -2.991619 -.6740973 2274356 0001332 1.182033 sigma_u 6.602254 sigma_e 4.576527 rho 67545035 (fraction of variance due to u_i) ụ ụ 12: T óp Variable pầ ạ Mean Std Dev Min Max Observations Res overall -2.86e-08 7.384262 -14.65421 21.56589 N = 150 between 6.602254 -8.65165 8.339896 n = within 4.451973 -15.3296 13.226 T-bar = 25 C ụ ụ 13: ị ễ2 Fisher-type unit-root test for Res Based on Phillips-Perron tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = 25.00 AR parameter: Panel-specific Panel means: Included Time trend: Not included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) P Inverse normal Z Inverse logit t(34) L* Modified inv chi-squared Pm p-value 38.9590 -3.4345 -4.0084 5.5030 0.0001 0.0003 0.0002 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ụ ụ 14: ị ó Fisher-type unit-root test for Res Based on Phillips-Perron tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = 25.00 AR parameter: Panel-specific Panel means: Included Time trend: Included Newey-West lags: lags Asymptotics: T -> Infinity Statistic Inverse chi-squared(12) Inverse normal Inverse logit t(34) P Z L* Modified inv chi-squared Pm p-value 27.8517 -1.8774 -2.2592 0.0058 0.0302 0.0152 3.2357 0.0006 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels ễ2 ụ ụ 15 ắ C óp Fixed-effects (within) regression Group variable: Id Number of obs = Number of groups = 14 R-sq: within = 0.2829 between = 0.0943 overall = 0.1369 Obs per group: avg = max = 16 23.8 26 corr(u_i, Xb) = -0.7221 F(4,5) Prob > F = = 12.93 0.0076 (Std Err adjusted for clusters in Id) Robust DTDGD Coef Std Err t P>|t| P DFDGD -.8726357 1944378 -4.49 0.006 DINFL 0677093 0286704 2.36 0.065 DlnEX 9.66e-06 0000441 0.22 0.835 LRes -.3231909 0774756 -4.17 0.009 _cons 20862 0794999 2.62 0.047 [95% Conf Interval] -1.372454 -.0059904 -.0001037 -.5223482 004259 sigma_ 2.2099128 sigma_e 3.1191434 rho 33420869 (fraction of variance due to u_i) ụ ụ 16: Hồ -.3728173 -.1240335 4129809 óp G DTDGD Fixed-effects (within) regression Group variable: Id Number of obs = Number of groups = 13 R-sq: within = 0.1779 between = 0.1352 overall = 0.1744 Obs per group: avg = max = 15 22.7 25 corr(u_i, Xb) = -0.1377 F(3,5) Prob > F = = 26.29 0.0017 (Std Err adjusted for clusters in Id) DTDGDP FDGDP L1 L2 Robust Coef.Std Err -.699591 3451381 _cons 5114747 sigma_u 40401588 sigma_e 3.3396397 t P>|t| [95% Conf Interval] 1433787 -4.88 0.005 2464672 2.32 0.068 0562303 6.14 0.002 3736074 1.37 0.229 -1.068158 -.3310244 -.0608657 1.20626 2005934 4896827 -.4489138 1.47186 rho 01442407(fraction of variance due to u_i) ụ ụ 17: Hồ óp G DGD Fixed-effects (within) regression Group variable: Id Number of obs = Number of groups = 12 R-sq: within = 0.0897 between = 0.2901 overall = 0.0487 Obs per group: avg = max = 14 21.5 24 corr(u_i, Xb) = -0.0480 F(3,5) Prob > F = = 7.80 0.0248 (Std Err adjusted for clusters in Id) FDGDP Robust Coef.Std Err t P>|t| [95% Conf Interval] DTDGD P -.0548115 0844699 -0.65 0.545 -.2719484 L1 -.1345203 0994474 -1.35 0.234 -.390158 L2 -.1068991 0509061 -2.10 0.090 -.2377573 _cons 0239592 -2.056881 0221692 -92.78 0.000 -2.113868 -1.999893 sigma_u 1.5869105 sigma_e 2.1500944 rho.35264216(fraction of variance due to u_i) ... ó ề ụ thâm ụ / ó thâm ụ / ĩ p Q thâm ụ G ụ/ ổ thâm ụ 1.2 thâm ụ / ị ó thâm thâm ụ / ý ép l T ó ẽ thâm ụ / yế ề mố quan hệ t h â m h t ý ó ó ụ p thâm h t pá ữ ụ 1.2.1 Thâm h t tài khóa tác đ ng... ề ụ ề ụ pầ á Đ L pá ữ ề ụ ề ụ ổ.Q ó, ề ĩ ó ó ữ thâm ụ ó ề ó ổ ó p áp óp ỏ thâm ụ ó Cá àm ý cho sách cho ụ ó ó ? P Đề tài ụ ữ thâm ụ pá á P ị p p áp cho ề ó , thâm ụ ơ p áp sau: ị p ổ ữ ị (1999)... quan h thâ m hụt 1.2.1 Thâm hụt tài khóa tác ó thâm 10 ng n thâm hụ 10 1.2.2 Thâm hụ có m ó Thâm ụ khơng uan h 1.2.3 Cá 11 ụ ụ ó 13 1.3 ? ?á á ề ụ ó ụ 15 21 2.1 2.2 21 p áp 2.2.1 ị 2.2.2 ị 22 ỗ