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?
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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.
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$\begingroup$ As it’s currently written, your answer is unclear. Please edit to add additional details that will help others understand how this addresses the question asked. You can find more information on how to write good answers in the help center. $\endgroup$– Community BotCommented Dec 1, 2021 at 9:33
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$\begingroup$ Is this complementary information about the question? $\endgroup$ Commented Dec 1, 2021 at 9:35
plm: cran.r-project.org/web/packages/plm/vignettes/A_plmPackage.html $\endgroup$