Hi guys,
I have run a linear regression and my model suffers from heteroskedasticity that's obvious. (see the table and test)
Normally what is done when heteroskedasticity occurs is add ,robust at the end of the model and the standard errors are taking count for the heteroskedasticity. I was wondering what is the best option when the model suffers from heteroskedasticity:
1. add robust to the model and continue using this corrected model with the robust standard errors.
2. bootstrap the regression (10000) times and use these model with the bootstrapped standard errors.
On my search on the internet i did not find a satisfying answer on what to choose maybe you guys could help me out in which way to handle the heteroskedasticity?
Thanks a lot!
Florian
Array
I have run a linear regression and my model suffers from heteroskedasticity that's obvious. (see the table and test)
Code:
reg HRQoL i.edu_cat i.smoking_behaviour blood_pressure i.bmi_categories cancer smoking_cancer COPD smoking_COPD diabetes blood_diabetes muskulo age gender age_gender marital_status age2 estat hettest, rhs iid
1. add robust to the model and continue using this corrected model with the robust standard errors.
2. bootstrap the regression (10000) times and use these model with the bootstrapped standard errors.
On my search on the internet i did not find a satisfying answer on what to choose maybe you guys could help me out in which way to handle the heteroskedasticity?
Thanks a lot!
Florian
Array