[R] Regression Testing

[Andrew Miles]

Perhaps the easiest way to incorporate the heteroskedasticity  
consistent SE's and output them in a familiar and easy to interpret  
format is to use coeftest() in the lmtest package.

coeftest(myModel, vcov=vcovHC(myModel))

Andrew Miles

On Jan 20, 2011, at 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.
>
>> 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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>>
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.