I am doing a regression analysis and my variable of interest turns out to be significant at the 5% level, but the model contains heteroskedasticity which can not be mitigated (using Box-Cox, Feasible generalized least squares).

Can I still conclude that my variable of interest is significant, or should I consider my result to be insignificant because of persistent heteroskedasticity?

Thank you!


You can conclude nothing: your parameters can be significant, but also not significant. If your data are heteroskedastic, you can use a correction to the standard error, that is Heteroskedasticity and Autocorrelation Consistent (HAC) covariance matrix of Newey and West: this computes standard errors robust to heteroskedasticity and you can conclude with more certanty about the significance of your parameters. Anyway, I think FGLS method is robust to heteroskedasticity.

  • $\begingroup$ Or Hubre-White robust standard errors..in R it is in sandwich package $\endgroup$ – Jan Sila Sep 12 '16 at 19:32

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