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felm() includes observations with zero weights when calculating the number of observations. This leads to different degrees of freedom and, hence, calculated statistics. Here is an example:
library(lfe)
cars<-mtcarscars[cars$carb==2,]$carb<-0#need to have some weights that are zeroreg_lfe<- felm(mpg~cyl,
weights=cars$carb,
data=cars)
reg_stats<- lm(mpg~cyl,
weights=carb,
data=cars)
nobs(reg_lfe)
nobs(reg_stats)
reg_lfe has 22 observations, and reg_stats has 32. They also have different F-statistics.
I was using felm() and comparing the reported F-stat from the first stage regression to the one computed manually using lm() and linearHypothesis(). I was getting similar, but not identical results. I realized that the reason was that I had a few observations with zero weights, which changed the F-statistic in felm(), so I think it's an important issue.
The text was updated successfully, but these errors were encountered:
felm()
includes observations with zero weights when calculating the number of observations. This leads to different degrees of freedom and, hence, calculated statistics. Here is an example:reg_lfe
has 22 observations, andreg_stats
has 32. They also have different F-statistics.I was using
felm()
and comparing the reported F-stat from the first stage regression to the one computed manually usinglm()
andlinearHypothesis()
. I was getting similar, but not identical results. I realized that the reason was that I had a few observations with zero weights, which changed the F-statistic infelm()
, so I think it's an important issue.The text was updated successfully, but these errors were encountered: