[英]R's sandwich package producing strange results for robust standard errors in linear model
I am trying to find heteroskedasticity-robust standard errors in R, and most solutions I find are to use the coeftest
and sandwich
packages. 我正在尝试在R中发现异方差稳健的标准错误,而我发现的大多数解决方案都是使用
coeftest
和sandwich
软件包。 However, when I use those packages, they seem to produce queer results (they're way too significant). 然而,当我使用这些软件包,他们似乎产生奇怪的结果(他们太显著)。 Both my professor and I agree that the results don't look right.
我的教授和我都同意结果看起来不正确。 Could someone please tell me where my mistake is?
有人可以告诉我我的错误在哪里吗? Am I using the right package?
我使用的是正确的包裹吗? Does the package have a bug in it?
程序包中是否有错误? What should I use instead?
我应该怎么用呢? Or can you reproduce the same results in STATA?
还是可以在STATA中重现相同的结果?
(The data is CPS data from 2010 to 2014, March samples. I created a MySQL database to hold the data and am using the survey
package to help analyze it.) (数据是2010年至2014年3月的样本的CPS数据。我创建了一个MySQL数据库来保存数据,并使用
survey
包来对其进行分析。)
Thank you in advance. 先感谢您。 (I have abridged the code somewhat to make it easier to read; let me know if you need to see more.)
(为了使代码更易于阅读,我对代码进行了一些简化;如果需要了解更多信息,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
The sandwich
package is object-oriented and essentially relies on two methods being available: estfun()
and bread()
, see the package vignettes for more details. 该
sandwich
包是面向对象的,基本上依赖于两种方法是可用: estfun()
和bread()
见包护身符的更多细节。 For objects of class svyglm
these methods are not available but as svyglm
objects inherit from glm
the glm
methods are found and used. 对于
svyglm
类的对象,这些方法不可用,但是由于svyglm
对象继承自glm
因此可以找到并使用glm
方法。 I suspect that this leads to incorrect results in the survey context though, possibly by a weighting factor or so. 我怀疑这可能会导致在权重因子左右的情况下在调查环境中得出错误的结果。 I'm not familiar enough with the
survey
package to provide a workaround. 我对
survey
包不够熟悉,无法提供解决方法。 The survey
maintainer might be able to say more... Hope that helps. survey
维护者也许可以说更多...希望能有所帮助。
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