Difference between revisions of "R robust se"
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== Heteroskedasticity robust standard errors == | == Heteroskedasticity robust standard errors == | ||
| − | I assume that you know that the presence of heteroskedastic standard errors renders OLS estimators of linear regression models inefficient (although they remain unbiased). More seriously, however, they also imply that the usual standard errors that are computed for your coefficient estimates (e.g. The <source enclose=none>summary()</source> as discussed in [R_Regression]) | + | I assume that you know that the presence of heteroskedastic standard errors renders OLS estimators of linear regression models inefficient (although they remain unbiased). More seriously, however, they also imply that the usual standard errors that are computed for your coefficient estimates (e.g. The <source enclose=none>summary()</source> as discussed in [[R_Regression]]) |
== Autocorrelation and heteroskedasticity robust standard errors == | == Autocorrelation and heteroskedasticity robust standard errors == | ||
Revision as of 20:50, 5 April 2015
Here we briefly discuss how to estimate robust standard errors for linear regression models
Contents
Which package to use
There are a number of pieces of code available to facilitate this task. Here I recommend to use the "sandwich" package. Which has the most comprehensive robust standard error options I am aware of.
Heteroskedasticity robust standard errors
I assume that you know that the presence of heteroskedastic standard errors renders OLS estimators of linear regression models inefficient (although they remain unbiased). More seriously, however, they also imply that the usual standard errors that are computed for your coefficient estimates (e.g. The summary() as discussed in R_Regression)
