研究数据普通数据

截面数据熵值法Stata计算案例

数据格式 Stata / Stata代码 / PDF更新日期

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

查看年份明细
年份样本量
201770

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37.6428571421.552117971740.0000%
year整数20170201720170.0000%
x1数值5.0227258573.394357174-0.4014869919.5435620.0000%
x2数值26.089880994.97139889515.95833335.50.0000%
x3数值16.583876144.7930845325.097446726.5646340.0000%
x4数值84.3240484136.2787381323.701656187.534730.0000%
x5数值76.6381818352.947322564.6454039244.101950.0000%

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。

idyearx1x2x3x4x5
120174.524211521.16666720.66746567.47227613.715511
220173.28658233.62519.896568105.1027996.061099
32017-0.4014869926.95833313.872921174.1589669.55268
4201711.33291520.3333335.097446747.4208542.470241
520173.532082130.523.773675162.7536754.962157
620179.883001518.08333313.77710182.48039540.793096
720176.636369422.41666718.66925723.70165658.078491
820170.137911563418.72720399.5365428.120436
920174.219903521.7515.8481117.7619265.575862
1120172.912135135.521.19534162.328623124.95601
1220173.340317429.511.91348872.205665101.29363
1320175.553897122.66666713.18005650.599832122.75369
1420173.415031925.79166716.0950138.66886944.718547
1520174.883012522.95833317.16435269.45106946.397457
1620173.28975373119.065333108.78816244.10195
1720171.917219128.45833320.158909138.7849848.665127
1820172.758682334.16666726.564634101.24574187.24101
1920178.7403599239.32214458.98966622.905218
2020174.475921615.95833312.7290564.49023925.340782
21201710.05391719.08333311.4506645.25563531.154923
2220175.128923824.2515.75590879.27662945.349897
2320173.416807535.37523.61519679.16990589.891247
2420172.11159829.2523.74452858.79057696.826322
2520172.075172931.12518.70767984.74773684.59802
2620178.726836822.58333313.78859886.29545415.050136
2720173.329864122.521.7861757.84462117.15516
2820176.215341821.66666710.18915863.98391423.572344
3020173.92992127.7520.655668167.3880458.833411
3120174.001026630.7524.610503104.76222140.91375
3220178.857845323.33333311.08446255.6238851.289233
3320175.356047823.2083339.058677250.18001330.082199
3520172.557188829.95833318.220617187.53473115.52977
3620173.45833529.70833322.36975671.49453369.040001
3720172.780632724.20833319.5984955.58298794.709016
3820177.529692423.37515.8102283.85034126.804855
392017-0.267633631.16666720.18778730.393003159.61627
4020178.452904724.58333310.47584873.11785735.144033
41201714.02249420.514.0111360.446530.571544
4220174.025846227.66666714.585726110.00005138.13329
4320174.839403525.20833314.88205699.11647760.806248
4420173.407376924.91666711.77708363.4696824.554339
4520177.68725120.33333321.842473130.8004235.896816
4620170.9146107126.91666718.70068383.426870.452056
4720172.341070232.79166725.999449146.17322115.82707
4820174.027906735.29166719.41746759.180458142.53341
4920178.08238822.41666713.59869111.8274526.202244
50201710.840028198.494302752.79410512.476314
5120171.284951633.66666721.00141169.596836125.61179
5220174.042188728.12518.044337115.7826741.183345
5320175.875802525.79166711.863957162.4875282.636449
5420178.25396831910.60445102.8226138.937509
5520173.37068522.2510.36235155.98828127.143958
5620174.71841724.5416679.70131967.69791631.86832
5720174.239401528.79166718.05142187.08272251.388861
5820173.65301125.16666719.85811972.863881156.19648
5920171.138366228.58333311.03909998.72163639.753025
6020175.789253322.41666714.95821579.68631239.109069
6120178.440464924.91666717.46343147.901142.840083
6220175.017157723.70833319.86222760.112632146.47976
6320173.196146426.79166720.52493958.086092166.75264
6420172.96115073425.18251488.661527126.41347
6520170.2313365833.511.681362122.49617159.96492
6620173.808790621.66666716.141798139.67556130.67308
6820173.544029423.518.000555105.8943676.494787
6920176.471879723.87513.70227952.66294149.424062
7020177.955724720.16666718.102125106.2418171.075042
7120174.463287431.7520.71958162.560013173.23452
7220173.156841632.91666716.30927430.885165179.06486
7320178.093775226.512.76045953.24703423.064382
74201719.54356217.91666712.79906863.0594684.6454039

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
	}