英文:
Robust F Test with PLM regression in R
问题
我正在尝试在R中使用PLM对象运行强健的F-Test。我正在运行以下代码：
library('wooldridge')data(wagepan, package='wooldridge')library('plm')pdata <- pdata.frame(wagepan, index=c("nr", "year"))fdreg <- plm(lwage ~ educ * (d81 + d82 + d83 + d84 + d85 + d86 + d87) + union, data=pdata, model="fd")summary(fdreg)library('car')H0 <- matchCoefs(fdreg, "educ")linearHypothesis(fdreg, H0)linearHypothesis(fdreg, H0, vcov=vcovHC(fdreg, "HC1"))
我遇到以下错误信息：
'arg' should be one of “arellano”, “white1”, “white2”
有人可以帮我解决这个问题吗？
英文:
I'm trying to run a robust F-Test using a PLM object in R. I'm running the following code:
library('wooldridge')data(wagepan,package='wooldridge')library('plm')pdata<-pdata.frame(wagepan,index=c("nr","year"))fdreg<-plm(lwage~educ*(d81+d82+d83+d84+d85+d86+d87)+union,data=pdata,model="fd")summary(fdreg)library('car')H0<-matchCoefs(fdreg,"educ")linearHypothesis(fdreg,H0)linearHypothesis(fdreg,H0,vcov=vcovHC(fdreg,"HC1"))
I'm getting the following error message:
'arg' should be one of “arellano”, “white1”, “white2”
Can anyone help me with this?
答案1
得分: 1
错误发生在linearHypothesis的第二行。
"HC1"是参数type的值，但是您输入的方式使其用于参数method，因为参数的顺序不对（请参考?plm::vcovHC了解参数及其顺序）。
我建议您更改您的行，明确指定参数，例如，
linearHypothesis(fdreg, H0, vcov. = vcovHC(fdreg, type = "HC1"))
线性假设检验
假设：
educ:d81 = 0
educ:d82 = 0
educ:d83 = 0
educ:d84 = 0
educ:d85 = 0
educ:d86 = 0
educ:d87 = 0
模型1：受限制模型
模型2：lwage ~ educ * (d81 + d82 + d83 + d84 + d85 + d86 + d87) + union
注意：提供了系数协方差矩阵。
Res.Df Df Chisq Pr(>Chisq)
1 3807
2 3800 7 8.0019 0.3324
英文:
The error occurs in the 2nd line for linearHypothesis.
"HC1" is a value for argument type but the way you input it, it is used for argument method due to order of arguments (see ?plm::vcovHC for the arguments and their order).
I suggest you change your line to specify the argument explicitly, e.g.,
linearHypothesis(fdreg, H0, vcov. = vcovHC(fdreg, type = "HC1"))Linear hypothesis testHypothesis:educ:d81 = 0educ:d82 = 0educ:d83 = 0educ:d84 = 0educ:d85 = 0educ:d86 = 0educ:d87 = 0Model 1: restricted modelModel 2: lwage ~ educ * (d81 + d82 + d83 + d84 + d85 + d86 + d87) + unionNote: Coefficient covariance matrix supplied.Res.Df Df Chisq Pr(>Chisq)1 38072 3800 7 8.0019 0.3324
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