Thank you for sharing these insightful imputation techniques. This is a valuable contribution for practitioners working with panel data in Stata. That said, it's important to emphasize that users should not only learn how to apply these techniques, but also clearly understand when and why they are appropriate, as well as the assumptions that underlie them.
Every imputation method, whether simple or advanced, alters the original dataset to some extent. While imputation is intended to mitigate the negative impacts of missing data, it also introduces new sources of uncertainty. If applied carelessly or without checking assumptions, imputation can result in biased estimates, misleading inferences, or spurious relationships in the data.