Thank you for this useful piece on imputation techniques in panel data analysis. I appreciate the simplicity, especially in walking us through single and multiple imputation in Stata.
From a micro-econometric context, where we typically handle household or individual-level data that corresponds to REAL INDIVIDUALS (and this is me definitely boasting about micro-econometrics, unapologetically...lol) and situations; handling missing data becomes even more crucial. In these contexts, missing values are not just a statistical inconvenience; they may reflect real-world limitations like survey fatigue, distrust, or access issues, which, if not properly accounted for, can lead to biased results and ill-informed policy recommendations.
In my own research on household surveys (e.g., displacement issues, health outcomes, labour force data, educational attainment), multiple imputation methods such as MICE are particularly handy. They allow us to include the uncertainty of missingness, especially when variables are inter-related to one another; e.g., income, labour market status, and levels of education. That being said, I would also caution that the MAR (Missing At Random) assumption needs to be rigorously theoretically justified. Some micro dataset missingness can be NMAR (Not Missing At Random); e.g., income nonresponse among the wealthiest households.
Another practical point: it is essential to combine multiple imputation with rigorous model diagnostics, for instance, checking convergence of imputations or comparing observed and imputed data distributions to achieve credibility, especially if findings will inform local or national-level interventions.
Looking forward to hearing how other individuals are applying these methods to community-focused datasets. Always happy to chat this!
