Random effects is like assuming your unobserved variables are playing nice with your predictors. Bold move! I’d only use it if I’m *super* confident.
Fixed effects is the safer bet, especially with short panels.
For Hausman, Stata’s `xtoverid` is underrated. And hey, Wooldridge’s panel data lectures on YouTube are gold.
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Wow, thanks for all the insights, everyone! Definitely gonna try the Hausman test in R’s `plm` package first—seems like the consensus pick.
Still a bit confused about when to *really* trust random effects, though. Like, how do you check if the unobserved stuff is truly uncorrelated? Any quick tricks besides the Hausman test?
Also, big shoutout for the resource recs—Wooldridge’s book is already in my cart! 🚀
Fixed effects is the safer bet, especially with short panels.
For Hausman, Stata’s `xtoverid` is underrated. And hey, Wooldridge’s panel data lectures on YouTube are gold.
---
Wow, thanks for all the insights, everyone! Definitely gonna try the Hausman test in R’s `plm` package first—seems like the consensus pick.
Still a bit confused about when to *really* trust random effects, though. Like, how do you check if the unobserved stuff is truly uncorrelated? Any quick tricks besides the Hausman test?
Also, big shoutout for the resource recs—Wooldridge’s book is already in my cart! 🚀