使用plm在R中聚集标准错误(具有固定效果) [英] Clustered standard errors in R using plm (with fixed effects)

问题描述

我试图在具有固定效果和model = 'within'的R的plm程序包中运行回归，同时使标准错误聚类.使用plm中的Cigar数据集，我正在运行:

I'm trying to run a regression in R's plm package with fixed effects and model = 'within', while having clustered standard errors. Using the Cigar dataset from plm, I'm running:

require(plm)
require(lmtest)
data(Cigar)
model <- plm(price ~ sales + factor(state), model = 'within', data = Cigar)
coeftest(model, vcovHC(model, type = 'HC0', cluster = 'group'))

  Estimate Std. Error t value Pr(>|t|)    
sales  -1.21956    0.21136 -5.7701 9.84e-09

(与)使用Stata(将Cigar文件写为.dta)所获得的(略)不同:

This is (slightly) different than what I'd get by using Stata (having written the Cigar file as a .dta):

use cigar

xtset state year

xtreg price sales, fe vce(cluster state)


price   Coef.   Std. Err.   t   P>t [95% Conf.  Interval]

sales   -1.219563   .2137726    -5.70   0.000   -1.650124   -.7890033

即，标准误差和T统计量不同.我尝试用不同的类型"重新运行R代码，但没有一个给出与Stata相同的结果.我想念什么吗?

Namely, the standard error and T statistic are different. I've tried rerunning the R code with different "types", but none give the same result as Stata. Am I missing something?

推荐答案

Stata使用有限样本校正以减少向下偏差由于群集数量有限而导致的错误.它是方差-协方差矩阵$ c = \ frac {G} {G-1} \ cdot \ frac {N-1} {NK} $的乘法因子，其中G是组数，N是观察数，K是参数数.我认为coeftest仅使用$ c'= \ frac {N-1} {NK} $，因为如果我按c中第一项的平方来缩放R的标准误差，我会得到一些与Stata的标准误差非常接近的信息:/p>

Stata uses a finite sample correction to reduce downwards bias in the errors due to the finite number of clusters. It is a multiplicative factor on the variance-covariance matrix, $c=\frac{G}{G-1} \cdot \frac{N-1}{N-K}$, where G is the number of groups, N is the number of observations, and K is the number of parameters. I think coeftest only uses $c'=\frac{N-1}{N-K}$ since if I scale R's standard error by the square of the first term in c, I get something pretty close to Stata's standard error:

display 0.21136*(46/(46-1))^(.5)
.21369554

这是我将如何复制Stata在R中所做的事情:

Here's how I would replicate what Stata is doing in R:

require(plm)
require(lmtest)
data(Cigar)
model <- plm(price ~ sales, model = 'within', data = Cigar)
G <- length(unique(Cigar$state))
c <- G/(G - 1)
coeftest(model,c * vcovHC(model, type = "HC1", cluster = "group"))

这将产生:

t test of coefficients:

       Estimate Std. Error  t value   Pr(>|t|)    
sales -1.219563   0.213773 -5.70496 1.4319e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

这与Stata的误差0.2137726和t-stat的-5.70一致.

which agrees with Stata's error of 0.2137726 and t-stat of -5.70.

此代码可能并不理想，因为数据中的状态数可能不同于回归中的状态数，但是我太懒了，无法弄清楚如何获得正确的面板数.

This code is probably not ideal, since the number of states in the data may be different than the number of states in the regression, but I am too lazy to figure out how to get the right number of panels.

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