Fixed Effects Panel regression with vce(cluster): Post-estimation problems

I am analyzing an unbalanced panel of firms over a 10 year period with 155 observations in 50 cross-sections. I ran a regression with both a fixed and random effects model and then used the Hausman-Test to find out which one is better suited. The Hausmann-test clearly rejected the random effects model. I then tested the fe model using xtreg, fe and reg with firm dummies for multicollinearity (VIF) and heteroskedasticity (xttest3, estat hettest). The null hypothesis of homoskedasticity was clearly rejected and the VIF was highly inflated (>70) for one of my control variables. I then switched to vce(cluster id) to tackle the heteroskedasticity problem and came to the conclusion that i can safely ignore the inflated VIF for a control variable. I am not interested in the variable's coefficient and only include it to increase the overall model fit and because its a standard control variable. My relevant explanatory variables have VIFs below 5.

Now, using the robust standard errors with vce(cluster id), xtreg still provides me with a F-Test-value, whereas reg does not anymore. Also, the standard errors obtained through the xtreg and reg command differ slightly after using the vce(robust id), while the coeffcients remain the same. I also delete singular clusters, but the issue nontheless persists. When I dont use robust standard errors both commands yield exactly the same result, as they should?

This brings me to the following questions:

• Can I still safely report the F-Test I get from xtreg, or is the missing F-Test in reg indicating that the F-Test from xtreg is biased?
• Which multicollinearity measure can I report, since the VIF from reg would then also be based on different standard errors than those obtained from xtreg?
• Given the robust standard errors, how can I compare different extensions of my base model (e.g. adding interaction terms, or other explanatory variables). The lrtest yields an error message referring to vce(cluster id)?

Overall, I have the impression that even though FE is the preferred choice in my case, the explanatory power of the model is rather weak, although some of the interaction effects are significant in a way in line with my theoretical baseline. Overall, I assume the amount of observation is too low. My fraction of variance due to u_i varies between 0.85 and 0.92 in my different variations. Is that value too high to even allow to draw meaningful conclusions?

Thanks and best regards
Andreas