Hello Everyone,

Let's say I would like to do a linear regression on some Panel data without knowing if linear regression is the most appropriate technique to use. Before doing any analysis, is it better to have more variables, and extrapolate and interpolate missing observations for a few of them (say perhaps 25% of the variables need this out of a total of 30), or is it better to delete the years that contain these missing observations? (especially if the raw data has a mismatch of years with and without observations).

There are also control variables that allow for the testing of models for years and countries excluding the extrapolated observations if it matters. Please see the attached picture for an idea of what I'm talking about.

I'm new to cleaning panel data I just want some insight on what to look out for and what to prioritize for significant results that don't compromise truth. Thank you!