On 1/20/2011 4:42 PM, Achim Zeileis wrote:
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.
I thought that WLS (which I guessing is a weighted regression) is really
only useful when you know or at least have an idea of what is causing
the Heteroskedasticity? I'm not familiar with FGLS. I plan on adding
additional independent variables as I get more comfortable with everything.
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/>.
I will look into that.
Thanks,
Mojo
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