我正在使用以下代码运行年度和县固定效果的基本差异差异回归模型：
xtreg ln_murder_rate i.treated##i.after_1980 i.year ln_deprivation ln_foreign_born young_population manufacturing low_skill_sector unemployment ln_median_income [weight = mean_population], fe cluster(fips) robust
i.treated 是一个二分法测量一个县是否在研究的整个生命周期内接受了治疗，并且测量了治疗的后期 . 但是，当我运行此回归时，我的治疗变量的估计值被省略，所以我无法真正解释结果 . 下面是输出的屏幕截图 . 我会喜欢一些关于检查内容的指导，这样我就可以在治疗前得到治疗变量的估计值 .
xtreg ln_murder_rate i.treated##i.after_1980 i.year ln_deprivation ln_foreign_bo
> rn young_population manufacturing low_skill_sector unemployment ln_median_income
> [weight = mean_population], fe cluster(fips) robust
(analytic weights assumed)
note: 1.treated omitted because of collinearity
note: 2000.year omitted because of collinearity
Fixed-effects (within) regression Number of obs = 15,221
Group variable: fips Number of groups = 3,117
R-sq: Obs per group:
within = 0.2269 min = 1
between = 0.1093 avg = 4.9
overall = 0.0649 max = 5
F(12,3116) = 89.46
corr(u_i, Xb) = 0.0502 Prob > F = 0.0000
(Std. Err. adjusted for 3,117 clusters in fips)
---------------------------------------------------------------------------------
| Robust
ln_murder_rate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.treated | 0 (omitted)
1.after_1980 | .2012816 .1105839 1.82 0.069 -.0155431 .4181063
|
treated#|
after_1980 |
1 1 | .0469658 .0857318 0.55 0.584 -.1211307 .2150622
|
year |
1970 | .4026329 .0610974 6.59 0.000 .2828376 .5224282
1980 | .6235034 .0839568 7.43 0.000 .4588872 .7881196
1990 | .4040176 .0525122 7.69 0.000 .3010555 .5069797
2000 | 0 (omitted)
|
ln_deprivation | .3500093 .119083 2.94 0.003 .1165202 .5834983
ln_foreign_born | .0179036 .0616842 0.29 0.772 -.1030421 .1388494
young_populat~n | .0030727 .0081619 0.38 0.707 -.0129306 .0190761
manufacturing | -.0242317 .0073166 -3.31 0.001 -.0385776 -.0098858
low_skill_sec~r | -.0084896 .0088702 -0.96 0.339 -.0258816 .0089025
unemployment | .0335105 .027627 1.21 0.225 -.0206585 .0876796
ln_median_inc~e | -.2423776 .1496396 -1.62 0.105 -.5357799 .0510246
_cons | 2.751071 1.53976 1.79 0.074 -.2679753 5.770118
----------------+----------------------------------------------------------------
sigma_u | .71424066
sigma_e | .62213091
rho | .56859936 (fraction of variance due to u_i)
---------------------------------------------------------------------------------
1 回答
这是偏离主题的，因为这基本上是一个统计问题 .
处理的变量被丢弃，因为它是时不变的，并且您正在进行固定效应回归，它通过减去每个协变量和结果的每个面板的平均值来转换数据 . 经过处理的观察结果都将处理设置为1，因此当您减去每个面板处理的平均值（也是一个）时，您得到零 . 类似地，对于对照观察，除了它们都已将处理设置为零 . 结果是处理后的色谱柱全部为零，Stata将其降低，因为否则基质不可逆，因为没有变化 .
您关心的参数将被处理#after_1980，这是DID效果并在输出中报告 . 治疗失败的事实并不令人担忧 .