A sample text widget

Etiam pulvinar consectetur dolor sed malesuada. Ut convallis euismod dolor nec pretium. Nunc ut tristique massa.

Nam sodales mi vitae dolor ullamcorper et vulputate enim accumsan. Morbi orci magna, tincidunt vitae molestie nec, molestie at mi. Nulla nulla lorem, suscipit in posuere in, interdum non magna.


If a model is estimated using the following code:

1. bptest(p) does the Breuch Pagan test to formally check presence of heteroscedasticity. To use bptest, you will have to call lmtest library.

2. If the test is positive (low p value), you should see if any transformation of the dependent variable helps you eliminate heteroscedasticity. Also check if the right hand side of the model is okay.

3. If 2 does not work, you can use the white’s heteroscedasticity-corrected covariance matrices to make inference. Package car has a function hccm that gives you the heteroscedasticity-corrected covariance matrix (there is a similar function in package sandwich also). coeftest(p,vcov=hccm(p)) will give you the results of the tests using this matrix. Use these results instead of summary(p).



Comments are closed.