Oh man, panel datasets are a beast! The biggest headache for me is definitely attrition. Like, you spend all this time cleaning the data, and then half your subjects just... disappear.
I’ve had some luck using multiple imputation (MI) to fill gaps, but it’s not perfect. The `mice` package in R is a lifesaver for this. Also, have you tried fixed effects models? They can help control for unobserved heterogeneity, which is a huge plus when dealing with unbalanced panels.
For computational load, consider downsizing your dataset or using tools like `dask` in Python if you’re working with massive files.
I’ve had some luck using multiple imputation (MI) to fill gaps, but it’s not perfect. The `mice` package in R is a lifesaver for this. Also, have you tried fixed effects models? They can help control for unobserved heterogeneity, which is a huge plus when dealing with unbalanced panels.
For computational load, consider downsizing your dataset or using tools like `dask` in Python if you’re working with massive files.