Panel datasets are like a puzzle where half the pieces are missing, lol.
One thing that helped me was using `pandas` in Python with its `ffill` and `bfill` methods for gaps. Not perfect, but better than nothing.
Also, if you’re dealing with attrition, maybe check out inverse probability weighting (IPW)? It’s a bit advanced, but it can help correct for bias.
And yeah, huge datasets suck. Cloud computing (like AWS or GCP) might be worth it if you’re stuck.
One thing that helped me was using `pandas` in Python with its `ffill` and `bfill` methods for gaps. Not perfect, but better than nothing.
Also, if you’re dealing with attrition, maybe check out inverse probability weighting (IPW)? It’s a bit advanced, but it can help correct for bias.
And yeah, huge datasets suck. Cloud computing (like AWS or GCP) might be worth it if you’re stuck.