0

我试图在 R 中找到异方差稳健的标准错误，我发现的大多数解决方案都是使用coeftestandsandwich包。然而，当我使用这些软件包时，它们似乎产生了奇怪的结果（它们太重要了）。我的教授和我都同意结果看起来不正确。有人可以告诉我我的错误在哪里吗？我使用了正确的包吗？包里面有bug吗？我应该改用什么？或者你能在 STATA 中重现相同的结果吗？

（数据是 2010 年到 2014 年的 CPS 数据，3 月份的样本。我创建了一个 MySQL 数据库来保存数据，并正在使用该survey包来帮助分析它。）

先感谢您。（我已经对代码进行了一些删节以使其更易于阅读；如果您需要查看更多内容，请告诉我。）

>male.nat.reg <- svyglm(log(HOURWAGE) ~ AGE + I(AGE^2) + ... + OVERWORK, subset(fwyrnat2010.design, FEMALE == 0))

>summary(male.nat.reg)

Call:
NextMethod(formula = "svyglm", design)

Survey design:
subset(fwyrnat2010.design, FEMALE == 0)

Coefficients:
           Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.599e+00  6.069e-02  26.350  < 2e-16 ***
AGE           4.030e-02  3.358e-03  12.000  < 2e-16 ***
I(AGE^2)     -4.131e-04  4.489e-05  -9.204 9.97e-16 ***
NOHSDEG      -1.730e-01  1.281e-02 -13.510  < 2e-16 ***
ASSOC         1.138e-01  1.256e-02   9.060 2.22e-15 ***
SOMECOLL      5.003e-02  9.445e-03   5.298 5.11e-07 ***
BACHELOR      2.148e-01  1.437e-02  14.948  < 2e-16 ***
GRADUATE      3.353e-01  3.405e-02   9.848  < 2e-16 ***
INMETRO       3.879e-02  9.225e-03   4.205 4.93e-05 ***
NCHILDOLD     1.374e-02  4.197e-03   3.273 0.001376 ** 
NCHILDYOUNG   2.334e-02  6.186e-03   3.774 0.000247 ***
NOTWHITE     -5.026e-02  8.583e-03  -5.856 3.92e-08 ***
MARRIED      -8.226e-03  1.531e-02  -0.537 0.592018    
NEVERMARRIED -4.644e-02  1.584e-02  -2.932 0.004009 ** 
NOTCITIZEN   -6.759e-02  1.574e-02  -4.295 3.47e-05 ***
STUDENT      -1.231e-01  1.975e-02  -6.231 6.52e-09 ***
VET           3.336e-02  1.751e-02   1.905 0.059091 .  
INUNION       2.366e-01  1.271e-02  18.614  < 2e-16 ***
PROFOCC       2.559e-01  1.661e-02  15.413  < 2e-16 ***
TSAOCC        9.997e-02  1.266e-02   7.896 1.27e-12 ***
FFFOCC        2.076e-02  2.610e-02   0.795 0.427859    
PRODOCC       2.164e-01  1.281e-02  16.890  < 2e-16 ***
LABOROCC      6.074e-02  1.253e-02   4.850 3.60e-06 ***
AFFIND        6.834e-02  2.941e-02   2.324 0.021755 *  
MININGIND     3.034e-01  3.082e-02   9.846  < 2e-16 ***
CONSTIND      1.451e-01  1.524e-02   9.524  < 2e-16 ***
MANUFIND      1.109e-01  1.393e-02   7.963 8.80e-13 ***
UTILIND       1.422e-01  1.516e-02   9.379 3.78e-16 ***
WHOLESALEIND  2.884e-02  1.766e-02   1.633 0.104910    
FININD        6.215e-02  2.084e-02   2.983 0.003436 ** 
BUSREPIND     6.588e-02  1.755e-02   3.753 0.000266 ***
SERVICEIND    5.412e-02  2.403e-02   2.252 0.026058 *  
ENTERTAININD -1.192e-01  3.060e-02  -3.896 0.000159 ***
PROFIND       1.536e-01  1.854e-02   8.285 1.55e-13 ***
OVERWORK      6.738e-02  1.007e-02   6.693 6.59e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.1367476)

Number of Fisher Scoring iterations: 2

>coeftest(male.nat.reg, vcov = vcovHC(male.nat.reg, type = 'HC0'))

z test of coefficients:

                Estimate  Std. Error   z value  Pr(>|z|)    
(Intercept)   1.5992e+00  9.7176e-08  16456481 < 2.2e-16 ***
AGE           4.0296e-02  5.4766e-09   7357823 < 2.2e-16 ***
I(AGE^2)     -4.1314e-04  7.3222e-11  -5642330 < 2.2e-16 ***
NOHSDEG      -1.7305e-01  1.4431e-08 -11991482 < 2.2e-16 ***
ASSOC         1.1378e-01  1.4248e-08   7985751 < 2.2e-16 ***
SOMECOLL      5.0035e-02  9.9689e-09   5019088 < 2.2e-16 ***
BACHELOR      2.1476e-01  2.0588e-08  10430993 < 2.2e-16 ***
GRADUATE      3.3533e-01  8.3327e-08   4024301 < 2.2e-16 ***
INMETRO       3.8790e-02  8.9666e-09   4326013 < 2.2e-16 ***
NCHILDOLD     1.3738e-02  5.2244e-09   2629554 < 2.2e-16 ***
NCHILDYOUNG   2.3344e-02  5.5405e-09   4213300 < 2.2e-16 ***
NOTWHITE     -5.0261e-02  1.0150e-08  -4951908 < 2.2e-16 ***
MARRIED      -8.2263e-03  1.8867e-08   -436026 < 2.2e-16 ***
NEVERMARRIED -4.6440e-02  1.7847e-08  -2602096 < 2.2e-16 ***
NOTCITIZEN   -6.7594e-02  2.4446e-08  -2765080 < 2.2e-16 ***
STUDENT      -1.2306e-01  3.2514e-08  -3785014 < 2.2e-16 ***
VET           3.3356e-02  3.0996e-08   1076125 < 2.2e-16 ***
INUNION       2.3659e-01  1.7786e-08  13301699 < 2.2e-16 ***
PROFOCC       2.5594e-01  2.2177e-08  11540563 < 2.2e-16 ***
TSAOCC        9.9971e-02  1.6707e-08   5983922 < 2.2e-16 ***
FFFOCC        2.0762e-02  2.3625e-08    878801 < 2.2e-16 ***
PRODOCC       2.1638e-01  1.3602e-08  15907683 < 2.2e-16 ***
LABOROCC      6.0741e-02  1.3445e-08   4517854 < 2.2e-16 ***
AFFIND        6.8342e-02  3.2895e-08   2077563 < 2.2e-16 ***
MININGIND     3.0343e-01  3.2948e-08   9209326 < 2.2e-16 ***
CONSTIND      1.4512e-01  2.1871e-08   6635457 < 2.2e-16 ***
MANUFIND      1.1094e-01  1.9636e-08   5649569 < 2.2e-16 ***
UTILIND       1.4216e-01  2.0930e-08   6792029 < 2.2e-16 ***
WHOLESALEIND  2.8842e-02  1.8662e-08   1545525 < 2.2e-16 ***
FININD        6.2147e-02  2.8214e-08   2202691 < 2.2e-16 ***
BUSREPIND     6.5883e-02  2.7866e-08   2364269 < 2.2e-16 ***
SERVICEIND    5.4118e-02  2.4758e-08   2185907 < 2.2e-16 ***
ENTERTAININD -1.1922e-01  2.9474e-08  -4044852 < 2.2e-16 ***
PROFIND       1.5364e-01  3.0132e-08   5098879 < 2.2e-16 ***
OVERWORK      6.7376e-02  1.0981e-08   6135525 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
4

1 回答 1

3

The sandwich package is object-oriented and essentially relies on two methods being available: estfun() and bread(), see the package vignettes for more details. For objects of class svyglm these methods are not available but as svyglm objects inherit from glm the glm methods are found and used. I suspect that this leads to incorrect results in the survey context though, possibly by a weighting factor or so. I'm not familiar enough with the survey package to provide a workaround. The survey maintainer might be able to say more... Hope that helps.

于 2015-03-03T19:58:10.467 回答