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*--------------------------虚拟变量--------------------------
use "E:\同步空间\EDUCATION\Economics\Econometrics\Introductory Econometrics A Modern Approach_Wooldrige\stata伍德里奇\WAGE1.DTA", clear
**虚拟变量分组功能检验--系数的计算
reg wage female //总方程1,其中female=1 , if 女性
*β_female=-2.51183
ttest wage , by(female)
*mean_wage_f=4.587659; mean_wage_m=7.099489
di 4.587659-7.099489 //β_female=mean_wage_f-mean_wage_m
**虚拟变量分组功能检验--分组方式进入回归
reg wage female educ //总方程1,其中female=1 , if 女性
*使用FWL定理
reg wage female //计算u1
predict u1,residual
reg educ female //计算u2
predict u2,residual
reg u1 u2 //FWL定理,β_u2=0.5064521
*检验残差 u_t=Y_t-mean_Y_t, u_c=Y_c-mean_Y_c
gen wage_f=wage-4.587659 //if female=1 , 则 u1=wage-mean_wage_f
replace wage_f=wage-7.099489 if female==0
ttest educ , by(female) //mean_edu_f=12.31746; mean_edu_m=12.78832
gen edu_f=educ-12.31746 //if female=1 , 则 u2=educ-mean_educ_f
replace edu_f=educ-12.78832 if female==0
reg wage_f edu_f //β_edu_f=β_u2=0.5064521
**虚拟变量图示
twoway (scatter wage educ if female==1) (lfit wage educ if female==1) (scatter wage educ if female==0) (lfit wage educ if female==0)
**虚拟变量=固定效应
reghdfe wage educ , absorb( female)
**虚拟变量的交互
reghdfe wage i.married##i.female , noabsorb
gen newv=married*female
reg wage married female newv
*-------------------------异方差--------------------------
reg wage female educ //不考虑异方差情形,假设误差项独立同分布
reg wage female educ, r //异方差-稳健标准误,允许协方差矩阵对角线元素不同
reg wage female educ, cl(smsa) //聚类-稳健标准误,允许协方差矩阵对角线元素不同、且聚类层面内非对角线元素不为0
**加权最小二乘法(WLS)
quietly: reg wage female educ //总方程1,其中female=1 , if 女性
predict u , residual //求残差
twoway (scatter u educ) //图示u的方差与educ之间的关系,大致发现u的方差与educ成正比关系
gen sqrt_educ=sqrt(educ)
reg wage female educ [aw=1/sqrt_educ] //按照1/sqrt_educ为权重,进行加权最小二乘法估计
*手动计算
gen wage_n=wage/sqrt_educ //手动计算
gen female_n=female/sqrt_educ
gen educ_n=educ/sqrt_educ
reg wage_n female_n educ_n
**广义加权最小二乘法(FLS)
gen u_sqr=u^2 //生产残差平方
gen lnu_sqr=ln(u_sqr) //取对数
reg lnu_sqr female educ //再回归
predict lnu_sqr_hat ,xb //求拟合值
gen u_hat=exp(lnu_sqr_hat) //去对数
reg wage female educ [aw=1/u_hat] //按照1/u_hat为权重,进行加权最小二乘法估计
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