Oh hey, I feel your pain! Panel datasets can be such a headache, especially with missing data.
I’ve had some success with `amelia` for multiple imputation—it’s pretty flexible for panel data. Also, if you’re using Stata, `mi impute` is solid.
For computational issues, try breaking your analysis into chunks or using `feols` from the `fixest` package. It’s lightning-fast for fixed effects models.
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Wow, thanks for all the awesome tips, everyone! Definitely gonna try `data.table` and `plm`—sounds like they’ll save me a ton of time.
Quick follow-up: anyone have experience with machine learning methods for imputing missing data in panel datasets? I’ve heard mixed things about random forests vs. MICE.
Also, shoutout to the cloud computing suggestion—my laptop is begging for mercy.
I’ve had some success with `amelia` for multiple imputation—it’s pretty flexible for panel data. Also, if you’re using Stata, `mi impute` is solid.
For computational issues, try breaking your analysis into chunks or using `feols` from the `fixest` package. It’s lightning-fast for fixed effects models.
---
Wow, thanks for all the awesome tips, everyone! Definitely gonna try `data.table` and `plm`—sounds like they’ll save me a ton of time.
Quick follow-up: anyone have experience with machine learning methods for imputing missing data in panel datasets? I’ve heard mixed things about random forests vs. MICE.
Also, shoutout to the cloud computing suggestion—my laptop is begging for mercy.