On Thu, 20 Jan 2011, Mojo wrote:
I'm new to R and some what new to the world of stats. I got frustrated with
excel and found R. Enough of that already.
I'm trying to test and correct for Heteroskedasticity
I have data in a csv file that I load and store in a dataframe.
ds <- read.csv("book2.csv")
df <- data.frame(ds)
I then preform a OLS regression:
lmfit <- lm(df$y~df$x)
Just btw: lm(y ~ x, data = df) is somewhat easier to read and also easier
to write when the formula involves more regressors.
To test for Heteroskedasticity, I run the BPtest:
bptest(lmfit)
studentized Breusch-Pagan test
data: lmfit
BP = 11.6768, df = 1, p-value = 0.0006329
From the above, if I'm interpreting this correctly, there is
Heteroskedasticity present. To correct for this, I need to calculate robust
error terms.
That is one option. Another one would be using WLS instead of OLS - or
maybe FGLS. As the model just has one regressor, this might be possible
and result in a more efficient estimate than OLS.
From my reading on this list, it seems like I need to vcovHC.
That's another option, yes.
vcovHC(lmfit)
(Intercept) df$x
(Intercept) 1.057460e-03 -4.961118e-05
df$x -4.961118e-05 2.378465e-06
I'm having a little bit of a hard time following the help pages.
Yes, the manual page is somewhat technical but the first thing the
"Details" section does is: It points you to some references that should be
easier to read. I recommend starting with
Zeileis A (2004), Econometric Computing with HC and HAC Covariance
Matrix Estimators. _Journal of Statistical Software_, *11*(10),
1-17. URL <URL: http://www.jstatsoft.org/v11/i10/>.
That has also some worked examples.
So is the first column the intercepts and the second column new standard
errors?
As David pointed out, it's the full covariance matrix estimate.
hth,
Z
Thanks,
mojo
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