Luận án Các mô hình kinh tế lượng đánh giá tác động của hạ tầng giao thông vận tải đến phát triển kinh tế tại Việt Nam

ông gian và nhiều dạng mô hình không gian. - Kết hợp thêm các biến giải thích, thêm các biến trễ thời gian hoặc đánh giá cho biến phụ thuộc là năng suất nhân tố tổng hợp (Total Factor Productivity - TFP). 101 DANH MỤC CÁC CÔNG TRÌNH TÁC GIẢ ĐÃ CÔNG BỐ 1. Lê Thị Quỳnh Nhung (2020), “Thực trạng vận tải hàng hóa đường biển khu vực Bắc Trung Bộ và kinh nghiệm chính sách vận tải biển tại Nhật Bản”, Hội thảo khoa học quốc gia Thực trạng phát triển và chính sách phát triển bền vững dải ven biển vùng Bắc Trung Bộ, Đại học Kinh tế quốc dân, tháng 6/2020. 2. Lê Thị Quỳnh Nhung (2020), "Tác động lan tỏa không gian của vốn đầu tư giao thông vận tải đến tăng trưởng kinh tế khu vực miền Trung", Tạp chí Khoa học và Đào tạo Ngân hàng, Số 223, tháng 12/2020, trang 26 - 33. 3. Lê Thị Quỳnh Nhung (2021), “Tác động của các loại hình giao thông tới tăng trưởng kinh tế: Nghiên cứu tại khu vực miền Nam”, Tạp chí Kinh tế và Dự báo, Số 9, tháng 3/2021, trang 128 - 131. 4. Lê Thị Quỳnh Nhung (2020), “Mô hình kinh tế lượng không gian đánh giá tác động của vốn đầu tư giao thông đến tăng trưởng kinh tế khu vực đồng bằng sông Hồng”, Tạp chí Công thương, Số 11/2020, trang 104 - 109. 5. Lê Thị Quỳnh Nhung (2020), “Tác động lan tỏa không gian của vốn đầu tư giao thông đến kinh tế tại Trung du miền núi phía Bắc và so sánh với vùng đồng bằng sông Hồng”, Tạp chí Công thương, Số 13/2020, trang 72 - 77. 102 TÀI LIỆU THAM KHẢO 1. Acemoglu, D. (2009), Introduction to modern economic growth, Princeton NJ: Princeton University Press. 2. Acemoglu, D., and Johnson, S. (2005), 'Unbundling Institutions', Journal of Political Economy, 113(5), 949 - 995. 3. Adams, S. (2009), 'Foreign Direct investment, domestic investment, and economic growth in Sub-Saharan Africa', Journal of Policy Modeling, 31(6), 939 - 949. 4. Aisen, A., and Veiga, F. (2013), 'How does political instability affect economic growth? European Journal of Political Economy, 29(C), 151 - 167. 5. 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(2008), 'Transport Infrastructure, Growth, and Poverty Alleviation: Empirical Analysis of China', Annals of Economics and Finance, 9(2), 345 - 371. 109 PHỤ LỤC Mô hình 1a xét tác động của của vốn đầu tư cho giao thông Kiểm định Lagrange (LM) lựa chọn giữa mô hình POLS và RE rho .96012822 (fraction of variance due to u_i) sigma_e .08173262 sigma_u .40107654 _cons -2.193325 2.35585 -0.93 0.355 -6.902602 2.515953 D .0790461 .0156644 5.05 0.000 .0477334 .1103588 LnTransparency .1443488 .0661093 2.18 0.033 .0121982 .2764994 LnEntryCosts .1629524 .0415514 3.92 0.000 .0798923 .2460125 LnLTraining_PCI .2610303 .0518631 5.03 0.000 .1573574 .3647032 LnTLRate .2281022 .0486433 4.69 0.000 .1308657 .3253387 LnL 1.068741 .1822488 5.86 0.000 .7044307 1.433051 LnKT .0478914 .0140482 3.41 0.001 .0198094 .0759734 LnKNT .1674246 .0336373 4.98 0.000 .1001845 .2346646 LnGRDP Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust (Std. Err. adjusted for 63 clusters in ID) corr(u_i, Xb) = 0.0959 Prob > F = 0.0000 F(8,62) = 111.44 overall = 0.8133 max = 8 between = 0.8120 avg = 8.0 within = 0.8439 min = 8 R-sq: Obs per group: Group variable: ID Number of groups = 63 Fixed-effects (within) regression Number of obs = 504 > fe rob . xtreg LnGRDP LnKNT LnKT LnL LnTLRate LnLTraining_PCI LnEntryCosts LnTransparency D, Prob > chibar2 = 0.0000 chibar2(01) = 1319.91 Test: Var(u) = 0 u .1139232 .3375251 e .0066802 .0817326 LnGRDP .8725719 .9341156 Var sd = sqrt(Var) Estimated results: LnGRDP[ID,t] = Xb + u[ID] + e[ID,t] Breusch and Pagan Lagrangian multiplier test for random effects . xttest0 110 Kiểm định Hausman lựa chọn mô hình 1a Kiểm định đa cộng tuyến (V_b-V_B is not positive definite) Prob>chi2 = 0.0001 = 29.48 chi2(7) = (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 D .0790461 .0744354 .0046107 .0058568 LnTranspar~y .1443488 .1456669 -.0013181 .0049515 LnEntryCosts .1629524 .1583321 .0046204 .0077151 LnLTrainin~I .2610303 .2546865 .0063438 .002958 LnTLRate .2281022 .2374498 -.0093476 .009424 LnL 1.068741 1.055879 .0128623 .1119366 LnKT .0478914 .0474866 .0004048 .0018475 LnKNT .1674246 .1831566 -.0157321 .0034525 fe re Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients coefficients are on a similar scale. anything unexpected and possibly consider scaling your variables so that the be problems computing the test. Examine the output of your estimators for coefficients being tested (8); be sure this is what you expect, or there may Note: the rank of the differenced variance matrix (7) does not equal the number of . hausman fe re, sig Mean VIF 2.42 ---------------------------------------------------- D 1.40 1.18 0.7135 0.2865 LnTransparency 1.23 1.11 0.8120 0.1880 LnEntryCosts 1.14 1.07 0.8808 0.1192 LnLTraining_PCI 3.06 1.75 0.3271 0.6729 LnTLRate 2.97 1.72 0.3371 0.6629 LnL 3.33 1.83 0.2999 0.7001 LnKT 2.03 1.42 0.4936 0.5064 LnKNT 4.17 2.04 0.2396 0.7604 ---------------------------------------------------- Variable VIF VIF Tolerance Squared SQRT R- Collinearity Diagnostics (obs=504) . collin LnKNT LnKT LnL LnTLRate LnLTraining_PCI LnEntryCosts LnTransparency D 111 Mô hình không gian SLX 1b xét tác động của vốn đầu tư cho giao thông Wald test of spatial terms: chi2(1) = 34.44 Prob > chi2 = 0.0000 /sigma_e .0776008 .0026188 .0726342 .082907 /sigma_u .4107921 .037328 .3437754 .4908733 LnKT .1629157 .0277625 5.87 0.000 .1085022 .2173292 M _cons -3.460151 1.006441 -3.44 0.001 -5.432739 -1.487563 D .0343479 .013518 2.54 0.011 .0078531 .0608426 LnTransparency .0941506 .0424176 2.22 0.026 .0110136 .1772876 LnEntryCosts .1351351 .0370573 3.65 0.000 .0625042 .207766 LnLTraining_PCI .1721441 .0470981 3.66 0.000 .0798336 .2644546 LnTLRate .1980595 .0342482 5.78 0.000 .1309344 .2651846 LnL 1.039943 .0770859 13.49 0.000 .8888576 1.191029 LnKT .0352967 .0090536 3.90 0.000 .0175519 .0530415 LnKNT .1659842 .0170036 9.76 0.000 .1326577 .1993107 LnGRDP LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = 402.5360 Pseudo R2 = 0.8036 Prob > chi2 = 0.0000 Wald chi2(9) = 2872.36 Obs per group = 8 Group variable: ID Number of groups = 63 Random-effects spatial regression Number of obs = 504 Iteration 1: log likelihood = 402.53596 (backed up) Iteration 0: log likelihood = 402.53596 Optimizing unconcentrated log likelihood: Iteration 4: log likelihood = 402.53596 Iteration 3: log likelihood = 402.53596 Iteration 2: log likelihood = 402.53586 Iteration 1: log likelihood = 402.50387 Iteration 0: log likelihood = 398.13307 rescale eq: log likelihood = 398.13307 rescale: log likelihood = 398.13307 improve: log likelihood = 398.13307 initial: log likelihood = 398.13307 Optimizing concentrated log likelihood: (weighting matrix defines 63 places) (data contain 63 panels (places) ) (504 observations used) (504 observations) > cy D, ivarlag(M: LnKT) re . spxtregress LnGRDP LnKNT LnKT LnL LnTLRate LnLTraining_PCI LnEntryCosts LnTransparen 112 Mô hình RE 2a xét tác động của vốn đầu tư cho giao thông tại khu vực Miền Bắc rho .87393198 (fraction of variance due to u_i) sigma_e .08345873 sigma_u .21973949 _cons -4.576295 .7616116 -6.01 0.000 -6.069026 -3.083564 D .0717648 .02219 3.23 0.001 .0282732 .1152563 LnTransparency .2413178 .0883342 2.73 0.006 .0681859 .4144497 LnEntryCosts .1188436 .0547557 2.17 0.030 .0115244 .2261628 LnTLRate .4216475 .0851241 4.95 0.000 .2548073 .5884877 LnL 1.063045 .0831968 12.78 0.000 .8999822 1.226108 LnKT .0937781 .0128577 7.29 0.000 .0685775 .1189787 LnKNT .2620777 .0415305 6.31 0.000 .1806794 .343476 LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust (Std. Err. adjusted for 25 clusters in ID) corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Wald chi2(7) = 1744.50 overall = 0.9226 max = 8 between = 0.9254 avg = 8.0 within = 0.8624 min = 8 R-sq: Obs per group: Group variable: ID Number of groups = 25 Random-effects GLS regression Number of obs = 200 > b . xtreg LnGRDP LnKNT LnKT LnL LnTLRate LnEntryCosts LnTransparency D if ID <=25, re ro 113 Mô hình không gian SLX 2b xét tác động của vốn đầu tư cho giao thông tại khu vực Miền Bắc Wald test of spatial terms: chi2(1) = 21.53 Prob > chi2 = 0.0000 /sigma_e .0785353 .0042244 .0706771 .0872673 /sigma_u .2882519 .0429361 .2152696 .3859772 LnKT .2285329 .0492549 4.64 0.000 .1319951 .3250707 M_s001 _cons -7.333448 1.337126 -5.48 0.000 -9.954167 -4.712728 D .010574 .0214951 0.49 0.623 -.0315557 .0527036 LnTransparency .1694829 .0583271 2.91 0.004 .0551639 .283802 LnEntryCosts .0739235 .0534979 1.38 0.167 -.0309305 .1787775 LnTLRate .3317214 .0631559 5.25 0.000 .2079381 .4555046 LnL 1.12549 .1010597 11.14 0.000 .9274165 1.323563 LnKT .080307 .015968 5.03 0.000 .0490103 .1116038 LnKNT .2259086 .0253508 8.91 0.000 .1762219 .2755953 LnGRDP LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = 166.4380 Pseudo R2 = 0.9138 Prob > chi2 = 0.0000 Wald chi2(8) = 1550.66 Obs per group = 8 Group variable: ID Number of groups = 25 Random-effects spatial regression Number of obs = 200 Iteration 1: log likelihood = 166.43805 (backed up) Iteration 0: log likelihood = 166.43805 Optimizing unconcentrated log likelihood: Iteration 4: log likelihood = 166.43805 Iteration 3: log likelihood = 166.43805 Iteration 2: log likelihood = 166.43796 Iteration 1: log likelihood = 166.41626 Iteration 0: log likelihood = 163.87094 rescale eq: log likelihood = 163.87094 rescale: log likelihood = 163.64097 improve: log likelihood = 163.64097 initial: log likelihood = 163.64097 Optimizing concentrated log likelihood: (weighting matrix M_s001 created) (weighting matrix matched 25 places in data) (you specified -force-) (weighting matrix defines 63 places) (data contain 25 panels (places) ) (200 observations used) (304 observations excluded due to if/in) (504 observations) > varlag(M: LnKT) force re . spxtregress LnGRDP LnKNT LnKT LnL LnTLRate LnEntryCosts LnTransparency D if ID <=25, i 114 Mô hình RE 3a xét tác động của vốn đầu tư cho giao thông tại khu vực Miền Trung rho .93813007 (fraction of variance due to u_i) sigma_e .06876409 sigma_u .26776475 _cons 1.155199 1.434276 0.81 0.421 -1.655931 3.966328 D .0708677 .019105 3.71 0.000 .0334226 .1083127 LnEntryCosts .110939 .0561999 1.97 0.048 .0007892 .2210888 LnLTraining_PCI .2483464 .08666 2.87 0.004 .078496 .4181969 LnTLRate .3915474 .0807927 4.85 0.000 .2331966 .5498982 LnL .9183692 .1122948 8.18 0.000 .6982756 1.138463 LnKT .0407635 .0190889 2.14 0.033 .0033499 .0781771 LnKNT .0815693 .0270603 3.01 0.003 .0285321 .1346065 LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust (Std. Err. adjusted for 19 clusters in ID) corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Wald chi2(7) = 760.52 overall = 0.8428 max = 8 between = 0.8395 avg = 8.0 within = 0.8846 min = 8 R-sq: Obs per group: Group variable: ID Number of groups = 19 Random-effects GLS regression Number of obs = 152 > 44, re rob . xtreg LnGRDP LnKNT LnKT LnL LnTLRate LnLTraining_PCI LnEntryCosts D if ID>=26& ID<= 115 Mô hình không gian SLX 3b xét tác động của vốn đầu tư cho giao thông tại Miền Trung . Wald test of spatial terms: chi2(1) = 9.80 Prob > chi2 = 0.0017 /sigma_e .065354 .0040212 .0579293 .0737303 /sigma_u .2324908 .0390178 .1673219 .3230418 LnKT .0889112 .0284072 3.13 0.002 .0332342 .1445882 M_s001 _cons .8844324 1.227635 0.72 0.471 -1.521687 3.290552 D .0504895 .01936 2.61 0.009 .0125447 .0884344 LnEntryCosts .0908292 .0596399 1.52 0.128 -.0260629 .2077213 LnLTraining_PCI .1664713 .0786795 2.12 0.034 .0122624 .3206803 LnTLRate .3494226 .0568063 6.15 0.000 .2380842 .460761 LnL .8848519 .0948925 9.32 0.000 .6988659 1.070838 LnKT .0291794 .0123505 2.36 0.018 .0049729 .0533859 LnKNT .0797412 .0270514 2.95 0.003 .0267214 .132761 LnGRDP LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = 155.0081 Pseudo R2 = 0.8388 Prob > chi2 = 0.0000 Wald chi2(8) = 1185.44 Obs per group = 8 Group variable: ID Number of groups = 19 Random-effects spatial regression Number of obs = 152 Iteration 1: log likelihood = 155.00808 (backed up) Iteration 0: log likelihood = 155.00808 Optimizing unconcentrated log likelihood: Iteration 3: log likelihood = 155.00808 Iteration 2: log likelihood = 155.00808 Iteration 1: log likelihood = 155.00802 Iteration 0: log likelihood = 154.96869 rescale eq: log likelihood = 154.96869 rescale: log likelihood = 154.96869 improve: log likelihood = 154.96869 initial: log likelihood = 154.96869 Optimizing concentrated log likelihood: (weighting matrix M_s001 created) (weighting matrix matched 19 places in data) (you specified -force-) (weighting matrix defines 63 places) (data contain 19 panels (places) ) (152 observations used) (352 observations excluded due to if/in) (504 observations) > D<= 44, ivarlag(M: LnKT) force re . spxtregress LnGRDP LnKNT LnKT LnL LnTLRate LnLTraining_PCI LnEntryCosts D if ID>=26& I 116 Mô hình FE 4a xét tác động của vốn đầu tư cho giao thông tại khu vực Miền Nam rho .98361262 (fraction of variance due to u_i) sigma_e .07327428 sigma_u .56768679 _cons 4.259601 3.65163 1.17 0.259 -3.41219 11.93139 D .1185983 .0203848 5.82 0.000 .0757714 .1614253 LnEntryCosts .2848703 .0600305 4.75 0.000 .1587509 .4109898 LnLTraining_PCI .257399 .0938901 2.74 0.013 .0601432 .4546547 LnTLRate .1302805 .0542926 2.40 0.027 .0162161 .244345 LnL .6461228 .3024393 2.14 0.047 .0107214 1.281524 LnKT .0290284 .0203663 1.43 0.171 -.0137596 .0718165 LnKNT .1694531 .0655354 2.59 0.019 .0317684 .3071378 LnGRDP Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust (Std. Err. adjusted for 19 clusters in ID) corr(u_i, Xb) = 0.5707 Prob > F = 0.0000 F(7,18) = 62.25 overall = 0.7741 max = 8 between = 0.7820 avg = 8.0 within = 0.8421 min = 8 R-sq: Obs per group: Group variable: ID Number of groups = 19 Fixed-effects (within) regression Number of obs = 152 > ob . xtreg LnGRDP LnKNT LnKT LnL LnTLRate LnLTraining_PCI LnEntryCosts D if ID >=45, fe r 117 Mô hình không gian SLX 4b xét tác động của vốn đầu tư cho giao thông tại Miền Nam Wald test of spatial terms: chi2(1) = 11.53 Prob > chi2 = 0.0007 /sigma_e .0684263 .0042257 .0606256 .0772307 /sigma_u .5328169 .0908963 .3813876 .7443709 LnKT .1512496 .0445466 3.40 0.001 .0639399 .2385594 M_s001 _cons .9696892 2.083454 0.47 0.642 -3.113805 5.053184 D .0616837 .0233766 2.64 0.008 .0158664 .1075009 LnEntryCosts .230762 .0671657 3.44 0.001 .0991197 .3624043 LnLTraining_PCI .1326153 .0864264 1.53 0.125 -.0367774 .302008 LnTLRate .1413867 .0494352 2.86 0.004 .0444955 .238278 LnL .7783511 .1614414 4.82 0.000 .4619319 1.09477 LnKT .0290602 .0156394 1.86 0.063 -.0015925 .059713 LnKNT .1531595 .0300279 5.10 0.000 .0943058 .2120131 LnGRDP LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = 133.2148 Pseudo R2 = 0.7604 Prob > chi2 = 0.0000 Wald chi2(8) = 811.09 Obs per group = 8 Group variable: ID Number of groups = 19 Random-effects spatial regression Number of obs = 152 Iteration 1: log likelihood = 133.21481 (backed up) Iteration 0: log likelihood = 133.21481 Optimizing unconcentrated log likelihood: Iteration 4: log likelihood = 133.21481 Iteration 3: log likelihood = 133.21481 Iteration 2: log likelihood = 133.21429 Iteration 1: log likelihood = 132.95051 Iteration 0: log likelihood = 132.74186 rescale eq: log likelihood = 132.74186 rescale: log likelihood = 126.8262 improve: log likelihood = 126.8262 initial: log likelihood = 126.8262 Optimizing concentrated log likelihood: (weighting matrix M_s001 created) (weighting matrix matched 19 places in data) (you specified -force-) (weighting matrix defines 63 places) (data contain 19 panels (places) ) (152 observations used) (352 observations excluded due to if/in) (504 observations) > ivarlag(M: LnKT) force re . spxtregress LnGRDP LnKNT LnKT LnL LnTLRate LnLTraining_PCI LnEntryCosts D if ID >=45, 118 Mô hình FE 5a xét tác động của các loại hình vốn đầu tư cho giao thông Kiểm định Lagrange (LM) lựa chọn giữa mô hình POLS và RE rho .96017169 (fraction of variance due to u_i) sigma_e .08232476 sigma_u .40421178 _cons -2.305118 2.311302 -1.00 0.322 -6.925344 2.315108 D .0784492 .0160189 4.90 0.000 .0464279 .1104705 LnTransparency .1378471 .0673922 2.05 0.045 .0031321 .2725622 LnEntryCosts .1500667 .041498 3.62 0.001 .0671133 .2330201 LnLTraining_PCI .2579381 .0509658 5.06 0.000 .1560589 .3598173 LnTLRate .2320662 .0501155 4.63 0.000 .1318869 .3322456 LnL 1.087174 .1794388 6.06 0.000 .728481 1.445867 LnKWTSupport .0070317 .0023791 2.96 0.004 .0022758 .0117875 LnKPRRoad .0324438 .0133951 2.42 0.018 .0056674 .0592203 LnKNT .1704863 .0335438 5.08 0.000 .1034332 .2375394 LnGRDP Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust (Std. Err. adjusted for 63 clusters in ID) corr(u_i, Xb) = 0.0632 Prob > F = 0.0000 F(9,62) = 100.92 overall = 0.8094 max = 8 between = 0.8080 avg = 8.0 within = 0.8420 min = 8 R-sq: Obs per group: Group variable: ID Number of groups = 63 Fixed-effects (within) regression Number of obs = 504 > LnTransparency D, fe rob . xtreg LnGRDP LnKNT LnKPRRoad LnKWTSupport LnL LnTLRate LnLTraining_PCI LnEntryCosts Prob > chibar2 = 0.0000 chibar2(01) = 1267.36 Test: Var(u) = 0 u .1056404 .3250236 e .0067774 .0823248 LnGRDP .8725719 .9341156 Var sd = sqrt(Var) Estimated results: LnGRDP[ID,t] = Xb + u[ID] + e[ID,t] Breusch and Pagan Lagrangian multiplier test for random effects . xttest0 119 Kiểm định Hausman lựa chọn giữa mô hình FE và RE Kiểm định đa cộng tuyến trong mô hình FE 5a Prob>chi2 = 0.0000 = 36.96 chi2(8) = (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 D .0784492 .0744451 .0040041 .0060888 LnTranspar~y .1378471 .1390289 -.0011818 .0051412 LnEntryCosts .1500667 .1433528 .0067139 .0080696 LnLTrainin~I .2579381 .2504425 .0074956 .0031973 LnTLRate .2320662 .2460342 -.0139679 .0100428 LnL 1.087174 1.058961 .0282128 .1147218 LnKWTSupport .0070317 .0085292 -.0014975 .0004112 LnKPRRoad .0324438 .027291 .0051528 .0018369 LnKNT .1704863 .1874631 -.0169768 .0036039 fe re Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients coefficients are on a similar scale. anything unexpected and possibly consider scaling your variables so that the be problems computing the test. Examine the output of your estimators for coefficients being tested (9); be sure this is what you expect, or there may Note: the rank of the differenced variance matrix (8) does not equal the number of . hausman fe re, sig Mean VIF 2.37 ---------------------------------------------------- D 1.40 1.18 0.7128 0.2872 LnTransparency 1.23 1.11 0.8115 0.1885 LnEntryCosts 1.15 1.07 0.8703 0.1297 LnLTraining_PCI 3.07 1.75 0.3255 0.6745 LnTLRate 3.01 1.73 0.3324 0.6676 LnL 3.53 1.88 0.2836 0.7164 LnKWTSupport 1.60 1.27 0.6245 0.3755 LnKPRRoad 1.73 1.32 0.5781 0.4219 LnKNT 4.64 2.15 0.2156 0.7844 ---------------------------------------------------- Variable VIF VIF Tolerance Squared SQRT R- Collinearity Diagnostics (obs=504) > sparency D . collin LnKNT LnKPRRoad LnKWTSupport LnL LnTLRate LnLTraining_PCI LnEntryCosts LnTran 120 Mô hình không gian 5b xét tác động của các loại hình vốn đầu tư cho giao thông . Wald test of spatial terms: chi2(2) = 76.31 Prob > chi2 = 0.0000 /sigma_e .0751891 .0025422 .0703681 .0803405 /sigma_u .3994559 .0367608 .3335302 .4784126 LnKWTSupport .0940846 .0135197 6.96 0.000 .0675864 .1205828 LnKPRRoad .066383 .0288086 2.30 0.021 .0099192 .1228468 M _cons -1.869242 1.006785 -1.86 0.063 -3.842504 .104019 D .0049861 .0142348 0.35 0.726 -.0229135 .0328857 LnTransparency .0677311 .0413345 1.64 0.101 -.013283 .1487452 LnEntryCosts .0271903 .0387634 0.70 0.483 -.0487845 .1031651 LnLTraining_PCI .2039063 .0456206 4.47 0.000 .1144916 .293321 LnTLRate .1894402 .0333746 5.68 0.000 .1240272 .2548533 LnL .9894797 .075739 13.06 0.000 .8410339 1.137925 LnKWTSupport .008691 .0025166 3.45 0.001 .0037585 .0136236 LnKPRRoad .0222301 .0080301 2.77 0.006 .0064914 .0379687 LnKNT .1584653 .016535 9.58 0.000 .1260572 .1908733 LnGRDP LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = 418.2225 Pseudo R2 = 0.8187 Prob > chi2 = 0.0000 Wald chi2(11) = 3078.17 Obs per group = 8 Group variable: ID Number of groups = 63 Random-effects spatial regression Number of obs = 504 Iteration 1: log likelihood = 418.2225 (backed up) Iteration 0: log likelihood = 418.2225 Optimizing unconcentrated log likelihood: Iteration 4: log likelihood = 418.2225 Iteration 3: log likelihood = 418.22249 Iteration 2: log likelihood = 418.21918 Iteration 1: log likelihood = 418.06465 Iteration 0: log likelihood = 411.97728 rescale eq: log likelihood = 411.97728 rescale: log likelihood = 410.17747 improve: log likelihood = 410.17747 initial: log likelihood = 410.17747 Optimizing concentrated log likelihood: (weighting matrix defines 63 places) (data contain 63 panels (places) ) (504 observations used) (504 observations) > sts LnTransparency D, ivarlag(M: LnKPRRoad LnKWTSupport) re . spxtregress LnGRDP LnKNT LnKPRRoad LnKWTSupport LnL LnTLRate LnLTraining_PCI LnEntryCo 121 Mô hình không gian 5c xét tác động của các loại hình vốn đầu tư cho giao thông Wald test of spatial terms: chi2(3) = 87.16 Prob > chi2 = 0.0000 /sigma_e .0740482 .0025042 .0692992 .0791226 /sigma_u .4004152 .0369033 .3342424 .4796887 LnKWaterways -.0378622 .0125085 -3.03 0.002 -.0623784 -.013346 LnKWTSupport .1007749 .0135599 7.43 0.000 .0741981 .1273518 LnKPRRoad .0678596 .0283791 2.39 0.017 .0122376 .1234817 M _cons -1.954949 1.001699 -1.95 0.051 -3.918243 .0083458 D .0160398 .0145971 1.10 0.272 -.01257 .0446496 LnTransparency .0689114 .0407319 1.69 0.091 -.0109216 .1487445 LnEntryCosts .0341564 .0385257 0.89 0.375 -.0413526 .1096653 LnLTraining_PCI .1988847 .044971 4.42 0.000 .1107432 .2870262 LnTLRate .1847843 .0328987 5.62 0.000 .1203041 .2492646 LnL 1.012552 .0759689 13.33 0.000 .8636561 1.161449 LnKWaterways .0031141 .001853 1.68 0.093 -.0005178 .0067459 LnKWTSupport .0090957 .002487 3.66 0.000 .0042211 .0139702 LnKPRRoad .0215344 .0079198 2.72 0.007 .0060119 .0370568 LnKNT .1566857 .0163152 9.60 0.000 .1247085 .188663 LnGRDP LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = 424.8193 Pseudo R2 = 0.8171 Prob > chi2 = 0.0000 Wald chi2(13) = 3180.96 Obs per group = 8 Group variable: ID Number of groups = 63 Random-effects spatial regression Number of obs = 504 Iteration 1: log likelihood = 424.81933 (backed up) Iteration 0: log likelihood = 424.81933 Optimizing unconcentrated log likelihood: Iteration 5: log likelihood = 424.81933 Iteration 4: log likelihood = 424.81933 Iteration 3: log likelihood = 424.81927 Iteration 2: log likelihood = 424.79575 Iteration 1: log likelihood = 420.38519 Iteration 0: log likelihood = 419.25123 rescale eq: log likelihood = 419.25123 rescale: log likelihood = 415.58959 improve: log likelihood = 415.58959 initial: log likelihood = 415.58959 Optimizing concentrated log likelihood: (weighting matrix defines 63 places) (data contain 63 panels (places) ) (504 observations used) (504 observations) > orce re > PCI LnEntryCosts LnTransparency D , ivarlag(M: LnKPRRoad LnKWTSupport LnKWaterways) f . spxtregress LnGRDP LnKNT LnKPRRoad LnKWTSupport LnKWaterways LnL LnTLRate LnLTraining_ 122 Mô hình FE 6.1a xét tác động của mật độ đường cao tốc Mô hình RE 6.2a xét tác động của mật độ đường cao tốc F test that all u_i=0: F(62, 182) = 213.87 Prob > F = 0.0000 rho .9970208 (fraction of variance due to u_i) sigma_e .0491398 sigma_u .89895058 _cons 17.01513 2.944732 5.78 0.000 11.20492 22.82533 Y2017 .2292189 .0135673 16.89 0.000 .2024495 .2559882 Y2016 .1576881 .0110858 14.22 0.000 .1358149 .1795613 Y2015 .0808709 .0096071 8.42 0.000 .0619152 .0998266 NaHWDensity .0000997 .0011472 0.09 0.931 -.0021638 .0023632 LnTLRate .0234926 .0435495 0.54 0.590 -.0624341 .1094194 LnL -.0498337 .2216713 -0.22 0.822 -.4872098 .3875424 LnKA .0543935 .0290064 1.88 0.062 -.0028385 .1116255 LnGRDP Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = 0.2382 Prob > F = 0.0000 F(7,182) = 123.67 overall = 0.1139 max = 4 between = 0.4284 avg = 4.0 within = 0.8263 min = 4 R-sq: Obs per group: Group variable: ID Number of groups = 63 Fixed-effects (within) regression Number of obs = 252 . xtreg LnGRDP LnKA LnL LnTLRate NaHWDensity Y2015 Y2016 Y2017 if Year >=2014, fe rho .98220324 (fraction of variance due to u_i) sigma_e .0491398 sigma_u .36505944 _cons .951871 1.202443 0.79 0.429 -1.404874 3.308616 Y2017 .1763813 .0135787 12.99 0.000 .1497675 .2029952 Y2016 .1228394 .0116333 10.56 0.000 .1000386 .1456401 Y2015 .0621336 .0106247 5.85 0.000 .0413095 .0829576 NaHWDensity .000427 .0012787 0.33 0.738 -.0020791 .0029331 LnTLRate .0871712 .0470137 1.85 0.064 -.004974 .1793164 LnL 1.061359 .0975583 10.88 0.000 .8701486 1.25257 LnKA .1112932 .0314772 3.54 0.000 .0495991 .1729873 LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Wald chi2(7) = 868.64 overall = 0.7704 max = 4 between = 0.7707 avg = 4.0 within = 0.7937 min = 4 R-sq: Obs per group: Group variable: ID Number of groups = 63 Random-effects GLS regression Number of obs = 252 . xtreg LnGRDP LnKA LnL LnTLRate NaHWDensity Y2015 Y2016 Y2017 if Year >=2014, re 123 Kiểm định Lagrange (LM) lựa chọn giữa mô hình POLS và RE Các kiểm định Hausman lựa chọn giữa mô hình 6.1a và 6.2a Prob > chibar2 = 0.0000 chibar2(01) = 337.28 Test: Var(u) = 0 u .1332684 .3650594 e .0024147 .0491398 LnGRDP .8518884 .922978 Var sd = sqrt(Var) Estimated results: LnGRDP[ID,t] = Xb + u[ID] + e[ID,t] Breusch and Pagan Lagrangian multiplier test for random effects . xttest0 see suest for a generalized test assumptions of the Hausman test; data fails to meet the asymptotic = -32.48 chi2 model fitted on these chi2(7) = (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 Y2017 .2292189 .1763813 .0528376 . Y2016 .1576881 .1228394 .0348488 . Y2015 .0808709 .0621336 .0187373 . NaHWDensity .0000997 .000427 -.0003273 . LnTLRate .0234926 .0871712 -.0636785 . LnL -.0498337 1.061359 -1.111193 .1990491 LnKA .0543935 .1112932 -.0568997 . fe re Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients . hausman fe re 124 Kiểm định hiện tượng tự tương quan tại mô hình 6.2a LM(Var(u)=0,lambda=0) = 337.57 Pr>chi2(2) = 0.0000 Joint Test: ALM(lambda=0) = 0.29 Pr>chi2(1) = 0.5915 Serial Correlation: ALM(Var(u)=0) = 12.61 Pr>N(0,1) = 0.0000 Random Effects, One Sided: ALM(Var(u)=0) = 158.93 Pr>chi2(1) = 0.0000 Random Effects, Two Sided: Tests: u .1332684 .36505944 e .0024147 .0491398 LnGRDP .8518884 .922978 Var sd = sqrt(Var) Estimated results: v[ID,t] = lambda v[ID,(t-1)] + e[ID,t] LnGRDP[ID,t] = Xb + u[ID] + v[ID,t] Tests for the error component model: . xttest1 125 Mô hình không gian 6b xét tác động của mật độ đường cao tốc trên cả nước Wald test of spatial terms: chi2(1) = 19.01 Prob > chi2 = 0.0000 /sigma_e .0486567 .002613 .0437956 .0540573 /sigma_u .5016617 .0505008 .4118348 .6110811 NaHWDensity .0272569 .0062511 4.36 0.000 .0150051 .0395088 M _cons 2.263802 1.528232 1.48 0.139 -.7314771 5.259082 Y2017 .1599086 .0139802 11.44 0.000 .1325079 .1873092 Y2016 .1075812 .0116314 9.25 0.000 .0847841 .1303783 Y2015 .027087 .012912 2.10 0.036 .00178 .052394 NaHWDensity .0012117 .0011437 1.06 0.289 -.0010298 .0034533 LnTLRate .0729456 .0423653 1.72 0.085 -.0100888 .15598 LnL 1.00046 .1152291 8.68 0.000 .7746149 1.226305 LnKA .0753558 .0289358 2.60 0.009 .0186426 .1320691 LnGRDP LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = 213.4850 Pseudo R2 = 0.7251 Prob > chi2 = 0.0000 Wald chi2(8) = 1003.44 Obs per group = 4 Group variable: ID Number of groups = 63 Random-effects spatial regression Number of obs = 252 Iteration 1: log likelihood = 213.48504 (backed up) Iteration 0: log likelihood = 213.48504 Optimizing unconcentrated log likelihood: Iteration 4: log likelihood = 213.48504 Iteration 3: log likelihood = 213.48504 Iteration 2: log likelihood = 213.48444 Iteration 1: log likelihood = 213.3326 Iteration 0: log likelihood = 210.91885 rescale eq: log likelihood = 210.91885 rescale: log likelihood = 197.16114 improve: log likelihood = 197.16114 initial: log likelihood = 197.16114 Optimizing concentrated log likelihood: (weighting matrix defines 63 places) (data contain 63 panels (places) ) (252 observations used) (252 observations excluded due to if/in) (504 observations) > arlag(M: NaHWDensity) force re . spxtregress LnGRDP LnKA LnL LnTLRate NaHWDensity Y2015 Y2016 Y2017 if Year>=2014, iv 126 Mô hình RE 7a xét tác động của mật độ đường cao tốc tại khu vực Quảng Ninh - Huế rho .96087859 (fraction of variance due to u_i) sigma_e .05019492 sigma_u .24876357 _cons -.8298606 1.629117 -0.51 0.610 -4.022872 2.363151 Y2017 .1812647 .03042 5.96 0.000 .1216426 .2408868 Y2016 .1310225 .0214179 6.12 0.000 .0890442 .1730008 Y2015 .0703949 .014191 4.96 0.000 .042581 .0982088 NaHWDensity .001407 .0011746 1.20 0.231 -.0008952 .0037092 LnTLRate .3559616 .1177723 3.02 0.003 .1251322 .586791 LnL 1.127176 .1334866 8.44 0.000 .8655475 1.388805 LnKA .1096353 .0515179 2.13 0.033 .008662 .2106085 LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust (Std. Err. adjusted for 31 clusters in ID) corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Wald chi2(7) = 794.85 overall = 0.8542 max = 4 between = 0.8546 avg = 4.0 within = 0.8347 min = 4 R-sq: Obs per group: Group variable: ID Number of groups = 31 Random-effects GLS regression Number of obs = 124 > 1, re rob . xtreg LnGRDP LnKA LnL LnTLRate NaHWDensity Y2015 Y2016 Y2017 if Year >=2014 & ID <=3 127 Mô hình không gian 7b xét tác động của mật độ đường cao tốc tại khu vực Quảng Ninh - Huế Wald test of spatial terms: chi2(1) = 15.63 Prob > chi2 = 0.0001 /sigma_e .0464046 .0035202 .0399935 .0538433 /sigma_u .3803183 .0533626 .2888782 .5007023 NaHWDensity .0331916 .0083947 3.95 0.000 .0167382 .049645 M_s001 _cons -.1900516 1.466905 -0.13 0.897 -3.065132 2.685028 Y2017 .1533546 .0232188 6.60 0.000 .1078466 .1988626 Y2016 .0888264 .0203451 4.37 0.000 .0489508 .128702 Y2015 -.0000657 .0235663 -0.00 0.998 -.0462548 .0461234 NaHWDensity .0024312 .0012861 1.89 0.059 -.0000895 .004952 LnTLRate .2268234 .0848163 2.67 0.007 .0605865 .3930603 LnL 1.148207 .1196315 9.60 0.000 .9137333 1.38268 LnKA .0575333 .0329179 1.75 0.081 -.0069845 .1220512 LnGRDP LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = 118.0188 Pseudo R2 = 0.8421 Prob > chi2 = 0.0000 Wald chi2(8) = 757.36 Obs per group = 4 Group variable: ID Number of groups = 31 Random-effects spatial regression Number of obs = 124 Iteration 1: log likelihood = 118.01885 (backed up) Iteration 0: log likelihood = 118.01885 Optimizing unconcentrated log likelihood: Iteration 4: log likelihood = 118.01885 Iteration 3: log likelihood = 118.01885 Iteration 2: log likelihood = 118.01511 Iteration 1: log likelihood = 117.50344 Iteration 0: log likelihood = 117.15057 rescale eq: log likelihood = 117.15057 rescale: log likelihood = 109.66494 improve: log likelihood = 109.66494 initial: log likelihood = 109.66494 Optimizing concentrated log likelihood: (weighting matrix M_s001 created) (weighting matrix matched 31 places in data) (you specified -force-) (weighting matrix defines 63 places) (data contain 31 panels (places) ) (124 observations used) (380 observations excluded due to if/in) (504 observations) > D <=31, ivarlag(M: NaHWDensity) force re . spxtregress LnGRDP LnKA LnL LnTLRate NaHWDensity Y2015 Y2016 Y2017 if Year>=2014 & I 128 Mô hình RE 8a xét tác động của mật độ đường cao tốc tại khu vực Đà Nẵng - Cà Mau Kiểm định tự tương quan của mô hình 8a rho .98345161 (fraction of variance due to u_i) sigma_e .04420078 sigma_u .34074431 _cons 2.179997 1.862795 1.17 0.242 -1.471013 5.831008 Y2017 .1353585 .0191415 7.07 0.000 .0978418 .1728751 Y2016 .0957751 .016648 5.75 0.000 .0631455 .1284046 Y2015 .0454423 .0148843 3.05 0.002 .0162696 .0746149 NaHWDensity -.0007064 .003007 -0.23 0.814 -.0066 .0051873 LnTLRate .0668492 .0551235 1.21 0.225 -.0411908 .1748892 LnL .9188522 .1506282 6.10 0.000 .6236264 1.214078 LnKA .1648978 .0580543 2.84 0.005 .0511135 .2786821 LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Wald chi2(7) = 363.80 overall = 0.7577 max = 4 between = 0.7590 avg = 4.0 within = 0.7709 min = 4 R-sq: Obs per group: Group variable: ID Number of groups = 32 Random-effects GLS regression Number of obs = 128 > 1, re . xtreg LnGRDP LnKA LnL LnTLRate NaHWDensity Y2015 Y2016 Y2017 if Year >=2014 & ID > 3 LM(Var(u)=0,lambda=0) = 168.43 Pr>chi2(2) = 0.0000 Joint Test: ALM(lambda=0) = 0.25 Pr>chi2(1) = 0.6175 Serial Correlation: ALM(Var(u)=0) = 8.82 Pr>N(0,1) = 0.0000 Random Effects, One Sided: ALM(Var(u)=0) = 77.74 Pr>chi2(1) = 0.0000 Random Effects, Two Sided: Tests: u .1161067 .34074431 e .0019537 .04420078 LnGRDP .7482438 .8650109 Var sd = sqrt(Var) Estimated results: v[ID,t] = lambda v[ID,(t-1)] + e[ID,t] LnGRDP[ID,t] = Xb + u[ID] + v[ID,t] Tests for the error component model: . xttest1 129 Mô hình không gian 8b xét tác động của mật độ đường cao tốc tại khu vực Đà Nẵng - Cà Mau Wald test of spatial terms: chi2(1) = 3.62 Prob > chi2 = 0.0572 /sigma_e .0468297 .0038208 .0399092 .0549502 /sigma_u .5115585 .0868128 .3668126 .7134217 NaHWDensity .0239811 .0126114 1.90 0.057 -.0007369 .048699 M_s001 _cons 5.105834 2.905184 1.76 0.079 -.588221 10.79989 Y2017 .1456491 .0213512 6.82 0.000 .1038015 .1874966 Y2016 .1092266 .0167433 6.52 0.000 .0764104 .1420428 Y2015 .0376157 .0159054 2.36 0.018 .0064417 .0687898 NaHWDensity -.0002737 .0026233 -0.10 0.917 -.0054154 .0048679 LnTLRate .0483648 .0496061 0.97 0.330 -.0488614 .1455911 LnL .7658312 .2026542 3.78 0.000 .3686363 1.163026 LnKA .114014 .0566826 2.01 0.044 .0029181 .2251099 LnGRDP LnGRDP Coef. Std. Err. z P>|z| [95% Conf. Interval] Log likelihood = 111.4900 Pseudo R2 = 0.7515 Prob > chi2 = 0.0000 Wald chi2(8) = 407.16 Obs per group = 4 Group variable: ID Number of groups = 32 Random-effects spatial regression Number of obs = 128 Iteration 1: log likelihood = 111.48995 (backed up) Iteration 0: log likelihood = 111.48995 Optimizing unconcentrated log likelihood: Iteration 4: log likelihood = 111.48995 Iteration 3: log likelihood = 111.48995 Iteration 2: log likelihood = 111.48972 Iteration 1: log likelihood = 111.39445 Iteration 0: log likelihood = 110.35316 rescale eq: log likelihood = 110.35316 rescale: log likelihood = 101.10642 improve: log likelihood = 101.10642 initial: log likelihood = 101.10642 Optimizing concentrated log likelihood: (weighting matrix M_s001 created) (weighting matrix matched 32 places in data) (you specified -force-) (weighting matrix defines 63 places) (data contain 32 panels (places) ) (128 observations used) (376 observations excluded due to if/in) (504 observations) > D > 31, ivarlag(M: NaHWDensity) force re . spxtregress LnGRDP LnKA LnL LnTLRate NaHWDensity Y2015 Y2016 Y2017 if Year>=2014 & I