研究数据普通数据
截面数据熵值法Stata计算案例
数据摘要
综合评价需要将不同单项指标转换为可比较的结果。本资料提供截面数据熵值法的Stata代码及示例数据,包含x1至x5等指标变量,便于结合代码观察计算流程。可用于学习截面指标赋权与综合评价的操作,并按实际研究问题调整变量。
基本信息
| 数据频率 | 年度 |
|---|---|
| 当前预览样本量 | 70 行 |
| 文件格式 | Stata / Stata代码 / PDF |
| 文件包大小 | 8.77 KB |
| 包含内容 | 数据表1份、代码1份 |
| 所属分类 |
样本年份分布
合计 70 行 · 1 个年份
2017
查看年份明细
| 年份 | 样本量 |
|---|---|
| 2017 | 70 |
测算方法
案例以x1、x2、x3为正向指标,x4、x5为负向指标,演示截面综合评价的熵值法。先分别采用(x−最小值)/(最大值−最小值)及(最大值−x)/(最大值−最小值)作极差归一化;计算每项标准化值占该列合计的比重p,再求E_j=−Σp ln(p)/ln(N),其中标准化值为0时将p ln(p)置0。令差异系数d_j=1−E_j,权重w_j=d_j/Σd_j,最后加总标准化值乘权重为Score。使用时替换示例指标并声明正负方向,代码按整份截面计算权重,不分年度。
以下字段、样本量及样本仅对应当前展示的数据表,不代表资料包全部文件。查看文件结构
字段列表共 7 个字段
| 字段 | 类型 | 均值 | 标准差 | 最小值 | 最大值 | 缺失率 |
|---|---|---|---|---|---|---|
id | 整数 | 37.64285714 | 21.55211797 | 1 | 74 | 0.0000% |
year | 整数 | 2017 | 0 | 2017 | 2017 | 0.0000% |
x1 | 数值 | 5.022725857 | 3.394357174 | -0.40148699 | 19.543562 | 0.0000% |
x2 | 数值 | 26.08988099 | 4.971398895 | 15.958333 | 35.5 | 0.0000% |
x3 | 数值 | 16.58387614 | 4.793084532 | 5.0974467 | 26.564634 | 0.0000% |
x4 | 数值 | 84.32404841 | 36.27873813 | 23.701656 | 187.53473 | 0.0000% |
x5 | 数值 | 76.63818183 | 52.94732256 | 4.6454039 | 244.10195 | 0.0000% |
数据样本预览
资料样本(部分数据)
仅可预览前100 行、100 列,滚动查看其余行列,完整数据以下载文件为准。
| id | year | x1 | x2 | x3 | x4 | x5 |
|---|---|---|---|---|---|---|
| 1 | 2017 | 4.5242115 | 21.166667 | 20.667465 | 67.472276 | 13.715511 |
| 2 | 2017 | 3.286582 | 33.625 | 19.896568 | 105.10279 | 96.061099 |
| 3 | 2017 | -0.40148699 | 26.958333 | 13.872921 | 174.15896 | 69.55268 |
| 4 | 2017 | 11.332915 | 20.333333 | 5.0974467 | 47.42085 | 42.470241 |
| 5 | 2017 | 3.5320821 | 30.5 | 23.773675 | 162.75367 | 54.962157 |
| 6 | 2017 | 9.8830015 | 18.083333 | 13.777101 | 82.480395 | 40.793096 |
| 7 | 2017 | 6.6363694 | 22.416667 | 18.669257 | 23.701656 | 58.078491 |
| 8 | 2017 | 0.13791156 | 34 | 18.727203 | 99.53654 | 28.120436 |
| 9 | 2017 | 4.2199035 | 21.75 | 15.8481 | 117.76192 | 65.575862 |
| 11 | 2017 | 2.9121351 | 35.5 | 21.195341 | 62.328623 | 124.95601 |
| 12 | 2017 | 3.3403174 | 29.5 | 11.913488 | 72.205665 | 101.29363 |
| 13 | 2017 | 5.5538971 | 22.666667 | 13.180056 | 50.599832 | 122.75369 |
| 14 | 2017 | 3.4150319 | 25.791667 | 16.09501 | 38.668869 | 44.718547 |
| 15 | 2017 | 4.8830125 | 22.958333 | 17.164352 | 69.451069 | 46.397457 |
| 16 | 2017 | 3.2897537 | 31 | 19.065333 | 108.78816 | 244.10195 |
| 17 | 2017 | 1.9172191 | 28.458333 | 20.158909 | 138.78498 | 48.665127 |
| 18 | 2017 | 2.7586823 | 34.166667 | 26.564634 | 101.24574 | 187.24101 |
| 19 | 2017 | 8.7403599 | 23 | 9.322144 | 58.989666 | 22.905218 |
| 20 | 2017 | 4.4759216 | 15.958333 | 12.72905 | 64.490239 | 25.340782 |
| 21 | 2017 | 10.053917 | 19.083333 | 11.45066 | 45.255635 | 31.154923 |
| 22 | 2017 | 5.1289238 | 24.25 | 15.755908 | 79.276629 | 45.349897 |
| 23 | 2017 | 3.4168075 | 35.375 | 23.615196 | 79.169905 | 89.891247 |
| 24 | 2017 | 2.111598 | 29.25 | 23.744528 | 58.790576 | 96.826322 |
| 25 | 2017 | 2.0751729 | 31.125 | 18.707679 | 84.747736 | 84.59802 |
| 26 | 2017 | 8.7268368 | 22.583333 | 13.788598 | 86.295454 | 15.050136 |
| 27 | 2017 | 3.3298641 | 22.5 | 21.78617 | 57.84462 | 117.15516 |
| 28 | 2017 | 6.2153418 | 21.666667 | 10.189158 | 63.983914 | 23.572344 |
| 30 | 2017 | 3.929921 | 27.75 | 20.655668 | 167.38804 | 58.833411 |
| 31 | 2017 | 4.0010266 | 30.75 | 24.610503 | 104.76222 | 140.91375 |
| 32 | 2017 | 8.8578453 | 23.333333 | 11.084462 | 55.62388 | 51.289233 |
| 33 | 2017 | 5.3560478 | 23.208333 | 9.0586772 | 50.180013 | 30.082199 |
| 35 | 2017 | 2.5571888 | 29.958333 | 18.220617 | 187.53473 | 115.52977 |
| 36 | 2017 | 3.458335 | 29.708333 | 22.369756 | 71.494533 | 69.040001 |
| 37 | 2017 | 2.7806327 | 24.208333 | 19.59849 | 55.582987 | 94.709016 |
| 38 | 2017 | 7.5296924 | 23.375 | 15.81022 | 83.850341 | 26.804855 |
| 39 | 2017 | -0.2676336 | 31.166667 | 20.187787 | 30.393003 | 159.61627 |
| 40 | 2017 | 8.4529047 | 24.583333 | 10.475848 | 73.117857 | 35.144033 |
| 41 | 2017 | 14.022494 | 20.5 | 14.01113 | 60.4465 | 30.571544 |
| 42 | 2017 | 4.0258462 | 27.666667 | 14.585726 | 110.00005 | 138.13329 |
| 43 | 2017 | 4.8394035 | 25.208333 | 14.882056 | 99.116477 | 60.806248 |
| 44 | 2017 | 3.4073769 | 24.916667 | 11.777083 | 63.46968 | 24.554339 |
| 45 | 2017 | 7.687251 | 20.333333 | 21.842473 | 130.80042 | 35.896816 |
| 46 | 2017 | 0.91461071 | 26.916667 | 18.700683 | 83.4268 | 70.452056 |
| 47 | 2017 | 2.3410702 | 32.791667 | 25.999449 | 146.17322 | 115.82707 |
| 48 | 2017 | 4.0279067 | 35.291667 | 19.417467 | 59.180458 | 142.53341 |
| 49 | 2017 | 8.082388 | 22.416667 | 13.59869 | 111.82745 | 26.202244 |
| 50 | 2017 | 10.840028 | 19 | 8.4943027 | 52.794105 | 12.476314 |
| 51 | 2017 | 1.2849516 | 33.666667 | 21.001411 | 69.596836 | 125.61179 |
| 52 | 2017 | 4.0421887 | 28.125 | 18.044337 | 115.78267 | 41.183345 |
| 53 | 2017 | 5.8758025 | 25.791667 | 11.863957 | 162.48752 | 82.636449 |
| 54 | 2017 | 8.2539683 | 19 | 10.60445 | 102.82261 | 38.937509 |
| 55 | 2017 | 3.370685 | 22.25 | 10.362351 | 55.988281 | 27.143958 |
| 56 | 2017 | 4.718417 | 24.541667 | 9.701319 | 67.697916 | 31.86832 |
| 57 | 2017 | 4.2394015 | 28.791667 | 18.051421 | 87.082722 | 51.388861 |
| 58 | 2017 | 3.653011 | 25.166667 | 19.858119 | 72.863881 | 156.19648 |
| 59 | 2017 | 1.1383662 | 28.583333 | 11.039099 | 98.721636 | 39.753025 |
| 60 | 2017 | 5.7892533 | 22.416667 | 14.958215 | 79.686312 | 39.109069 |
| 61 | 2017 | 8.4404649 | 24.916667 | 17.463431 | 47.9011 | 42.840083 |
| 62 | 2017 | 5.0171577 | 23.708333 | 19.862227 | 60.112632 | 146.47976 |
| 63 | 2017 | 3.1961464 | 26.791667 | 20.524939 | 58.086092 | 166.75264 |
| 64 | 2017 | 2.9611507 | 34 | 25.182514 | 88.661527 | 126.41347 |
| 65 | 2017 | 0.23133658 | 33.5 | 11.681362 | 122.49617 | 159.96492 |
| 66 | 2017 | 3.8087906 | 21.666667 | 16.141798 | 139.67556 | 130.67308 |
| 68 | 2017 | 3.5440294 | 23.5 | 18.000555 | 105.89436 | 76.494787 |
| 69 | 2017 | 6.4718797 | 23.875 | 13.702279 | 52.662941 | 49.424062 |
| 70 | 2017 | 7.9557247 | 20.166667 | 18.102125 | 106.24181 | 71.075042 |
| 71 | 2017 | 4.4632874 | 31.75 | 20.719581 | 62.560013 | 173.23452 |
| 72 | 2017 | 3.1568416 | 32.916667 | 16.309274 | 30.885165 | 179.06486 |
| 73 | 2017 | 8.0937752 | 26.5 | 12.760459 | 53.247034 | 23.064382 |
| 74 | 2017 | 19.543562 | 17.916667 | 12.799068 | 63.059468 | 4.6454039 |
代码公开披露
仅展示前 30 行,供了解变量构造与处理流程,完整代码请下载文件查看。
//将你的数据dta,取名数据,和这个do文件放在一个文件夹
use 数据
global positive_var x1 x2 x3
global negative_var x4 x5
global all_var $positive_var $negative_var
foreach i in $positive_var {
qui sum `i'
gen x_`i'=(`i'-r(min))/(r(max)-r(min))
}
foreach i in $negative_var {
qui sum `i'
gen x_`i'=(r(max)-`i')/(r(max)-r(min))
}
foreach i in $all_var {
egen `i'_sum=sum(x_`i')
gen y_`i'=x_`i'/`i'_sum
}
gen n=_N
foreach i in $all_var {
gen y_lny_`i'=y_`i'*ln(y_`i')
replace y_lny_`i'=0 if x_`i'==0
}
foreach i in $all_var {
egen y_lny_`i'_sum=sum(y_lny_`i')
}
foreach i in $all_var {
gen E_`i'= -1/ln(n)*y_lny_`i'_sum
}