I am a Stata user and am trying to determine the most appropriate method for improving statistical inference in GLM (generalized linear models) applications with very few clusters (in one study G=29 and in another G=4 - I am aware of Webb's 2014 advice on 6-point weights for studies with fewer than 11 clusters). The clusters are unbalanced and I've got heteroskedasticity.
Please take as given that I need to use GLM rather than OLS for these studies.
Is it appropriate to use the wild cluster bootstrap procedure with GLMs? Recent explanations of the wild method indicate that the first step is to be completed using OLS, and Stata packages (e.g., CLUSTSE and CGMWILDBOOT) note restrictions to -regress- commands. It seems to me, however, that I should be ok using Wild cluster bootstrap with GLM because the error component of the GLM is additively separable.
Any thoughts or advice on where to look for further instruction on combining GLM and wild cluster bootstrap will be greatly appreciated!