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ex3.log.tex 6.27 KB
一键复制 编辑 原始数据 按行查看 历史
kerrydu 提交于 2021-10-22 17:41 . revised for sj
.
. egen id=group(Province)
{\smallskip}
. xtset id year
{\smallskip}
Panel variable: id (strongly balanced)
Time variable: year, 2013 to 2015
Delta: 1 unit
{\smallskip}
. gtfpch K L= Y: CO2, dmu( Province ) global sav(ex3result,replace)
{\smallskip}
The directional vector is (0 0 Y -CO2)
{\smallskip}
{\smallskip}
Total Factor Productivity Change:Malmquist-Luenberger Productivity Index
(Row: Row \# in the original data; Pdwise: periodwise)
{\smallskip}
{\TLC}\HLI{64}{\TRC}
{\VBAR} Row Province id Pdwise TFPCH TECH TECCH {\VBAR}
{\LFTT}\HLI{64}{\RGTT}
1. {\VBAR} 2 Anhui 1 2013{\tytilde}2014 0.9943 0.9662 1.0290 {\VBAR}
2. {\VBAR} 3 Anhui 1 2014{\tytilde}2015 0.9951 0.9645 1.0317 {\VBAR}
3. {\VBAR} 5 Beijing 2 2013{\tytilde}2014 1.0328 1.0000 1.0328 {\VBAR}
4. {\VBAR} 6 Beijing 2 2014{\tytilde}2015 1.0583 1.0000 1.0583 {\VBAR}
5. {\VBAR} 8 Chongqing 3 2013{\tytilde}2014 1.0013 0.9883 1.0132 {\VBAR}
6. {\VBAR} 9 Chongqing 3 2014{\tytilde}2015 1.0222 0.9659 1.0582 {\VBAR}
7. {\VBAR} 11 Fujian 4 2013{\tytilde}2014 0.9813 0.9438 1.0397 {\VBAR}
8. {\VBAR} 12 Fujian 4 2014{\tytilde}2015 1.0207 0.9774 1.0443 {\VBAR}
9. {\VBAR} 14 Gansu 5 2013{\tytilde}2014 0.9942 0.9748 1.0200 {\VBAR}
10. {\VBAR} 15 Gansu 5 2014{\tytilde}2015 0.9958 0.9725 1.0240 {\VBAR}
11. {\VBAR} 17 Guangdong 6 2013{\tytilde}2014 1.0185 0.9575 1.0637 {\VBAR}
12. {\VBAR} 18 Guangdong 6 2014{\tytilde}2015 1.0152 0.9841 1.0316 {\VBAR}
13. {\VBAR} 20 Guangxi 7 2013{\tytilde}2014 1.0031 0.9856 1.0177 {\VBAR}
14. {\VBAR} 21 Guangxi 7 2014{\tytilde}2015 1.0317 0.9830 1.0495 {\VBAR}
15. {\VBAR} 23 Guizhou 8 2013{\tytilde}2014 1.0014 0.9889 1.0127 {\VBAR}
16. {\VBAR} 24 Guizhou 8 2014{\tytilde}2015 1.0080 0.9879 1.0204 {\VBAR}
17. {\VBAR} 26 Hainan 9 2013{\tytilde}2014 0.9729 0.9497 1.0244 {\VBAR}
18. {\VBAR} 27 Hainan 9 2014{\tytilde}2015 0.9773 0.9321 1.0485 {\VBAR}
19. {\VBAR} 29 Hebei 10 2013{\tytilde}2014 1.0052 0.9796 1.0261 {\VBAR}
20. {\VBAR} 30 Hebei 10 2014{\tytilde}2015 1.0002 0.9784 1.0223 {\VBAR}
21. {\VBAR} 32 Heilongjiang 11 2013{\tytilde}2014 1.0007 0.9546 1.0482 {\VBAR}
22. {\VBAR} 33 Heilongjiang 11 2014{\tytilde}2015 1.0071 0.9854 1.0220 {\VBAR}
23. {\VBAR} 35 Henan 12 2013{\tytilde}2014 0.9955 0.9689 1.0274 {\VBAR}
24. {\VBAR} 36 Henan 12 2014{\tytilde}2015 0.9963 0.9632 1.0343 {\VBAR}
25. {\VBAR} 38 Hubei 13 2013{\tytilde}2014 1.0019 0.9618 1.0418 {\VBAR}
26. {\VBAR} 39 Hubei 13 2014{\tytilde}2015 1.0015 0.9656 1.0372 {\VBAR}
27. {\VBAR} 41 Hunan 14 2013{\tytilde}2014 1.0090 0.9714 1.0388 {\VBAR}
28. {\VBAR} 42 Hunan 14 2014{\tytilde}2015 0.9820 0.9453 1.0388 {\VBAR}
29. {\VBAR} 44 Jiangsu 15 2013{\tytilde}2014 1.0275 0.9894 1.0385 {\VBAR}
30. {\VBAR} 45 Jiangsu 15 2014{\tytilde}2015 1.0390 0.9975 1.0417 {\VBAR}
31. {\VBAR} 47 Jiangxi 16 2013{\tytilde}2014 0.9932 0.9731 1.0206 {\VBAR}
32. {\VBAR} 48 Jiangxi 16 2014{\tytilde}2015 0.9970 0.9515 1.0478 {\VBAR}
33. {\VBAR} 50 Jilin 17 2013{\tytilde}2014 1.0064 0.9858 1.0209 {\VBAR}
34. {\VBAR} 51 Jilin 17 2014{\tytilde}2015 1.0312 0.9922 1.0393 {\VBAR}
35. {\VBAR} 53 Liaoning 18 2013{\tytilde}2014 1.0179 0.9712 1.0481 {\VBAR}
36. {\VBAR} 54 Liaoning 18 2014{\tytilde}2015 1.0289 0.9997 1.0292 {\VBAR}
37. {\VBAR} 56 Neimenggu 19 2013{\tytilde}2014 1.0074 0.9830 1.0249 {\VBAR}
38. {\VBAR} 57 Neimenggu 19 2014{\tytilde}2015 1.0157 1.0019 1.0138 {\VBAR}
39. {\VBAR} 59 Ningxia 20 2013{\tytilde}2014 1.0024 0.9966 1.0058 {\VBAR}
40. {\VBAR} 60 Ningxia 20 2014{\tytilde}2015 1.0021 0.9914 1.0108 {\VBAR}
41. {\VBAR} 62 Qinghai 21 2013{\tytilde}2014 1.0127 0.9928 1.0200 {\VBAR}
42. {\VBAR} 63 Qinghai 21 2014{\tytilde}2015 1.0038 0.9657 1.0395 {\VBAR}
43. {\VBAR} 65 Shaanxi 22 2013{\tytilde}2014 0.9983 0.9908 1.0076 {\VBAR}
44. {\VBAR} 66 Shaanxi 22 2014{\tytilde}2015 1.0120 0.9772 1.0357 {\VBAR}
45. {\VBAR} 68 Shandong 23 2013{\tytilde}2014 1.0032 0.9588 1.0463 {\VBAR}
46. {\VBAR} 69 Shandong 23 2014{\tytilde}2015 0.9919 0.9685 1.0241 {\VBAR}
47. {\VBAR} 71 Shanghai 24 2013{\tytilde}2014 0.9955 1.0000 0.9955 {\VBAR}
48. {\VBAR} 72 Shanghai 24 2014{\tytilde}2015 1.0152 1.0000 1.0152 {\VBAR}
49. {\VBAR} 74 Shanxi 25 2013{\tytilde}2014 0.9932 0.9788 1.0147 {\VBAR}
50. {\VBAR} 75 Shanxi 25 2014{\tytilde}2015 0.9967 0.9855 1.0114 {\VBAR}
51. {\VBAR} 77 Sichuan 26 2013{\tytilde}2014 1.0027 0.9699 1.0338 {\VBAR}
52. {\VBAR} 78 Sichuan 26 2014{\tytilde}2015 1.0187 0.9774 1.0423 {\VBAR}
53. {\VBAR} 80 Tianjin 27 2013{\tytilde}2014 1.0686 1.0000 1.0686 {\VBAR}
54. {\VBAR} 81 Tianjin 27 2014{\tytilde}2015 1.0697 0.9701 1.1027 {\VBAR}
55. {\VBAR} 83 Xinjiang 28 2013{\tytilde}2014 0.9897 0.9746 1.0154 {\VBAR}
56. {\VBAR} 84 Xinjiang 28 2014{\tytilde}2015 0.9881 0.9727 1.0159 {\VBAR}
57. {\VBAR} 86 Yunnan 29 2013{\tytilde}2014 1.0161 0.9889 1.0275 {\VBAR}
58. {\VBAR} 87 Yunnan 29 2014{\tytilde}2015 1.0155 0.9849 1.0310 {\VBAR}
59. {\VBAR} 89 Zhejiang 30 2013{\tytilde}2014 1.0143 0.9677 1.0481 {\VBAR}
60. {\VBAR} 90 Zhejiang 30 2014{\tytilde}2015 1.0028 0.9695 1.0343 {\VBAR}
{\BLC}\HLI{64}{\BRC}
Note: missing value indicates infeasible problem.
(file{\bftt{ ex3result.dta}} not found)
file{\bftt{ ex3result.dta}} saved
{\smallskip}
Estimated Results are saved in ex3result.dta.
{\smallskip}
.
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