Fixed effects vs. random effects in plm I have unbalanced panel data, with different observations on different companies over a 10 year period. However, when choosing whether to use Fixed effects or Random effects I did a Hausman test to decide, and the p value vas < 0.05 so fixed effect is preferable. However, R adjusted is negative when using fixed effects and positive (and good) when using random effects? Is there any way where we can use the random effects despite the test, or is there a way of improving the R adjusted in the fixed effects model?

The sample is drawn form a large population. The R2 = 0.018 and R adjusted is -0.15 for the fixed effects model, and R2= 0.305 and R adjusted is R2 adjusted = 0.302. The dependent variable is ESG score and the independent variables are economic metrics as well as a dummy to be able and divide the companies into two groups.
