I am also facing the similar situation with low adjusted R square value for panel data random effect model (~30%). But with the same set of variables fixed effect model (LSDV) shows more than 90% value for adjusted R-square. My data set is long panel i.e. number of cross sections is very high. Because of the long panel and confirmed by Hausman test, I need to use random effect model.
From the different posts appearing in Research Gate, I understand that low adjusted r-square for random effect should not be worried as long as all variables are significant and backed up by literature.
I am curious to know why adjusted R-square value differs so much between fixed and random effect. Also, if anyone can provide reference of book or research paper where authors mentioned about low adjusted r-square of panel data random effect then I will be highly obliged. Thanks in advance.
- Badges
- Science topic