Normally, I exclude a covariate out of the regression equation if there are many missing observations, let's say it is one-fifth less observation compared to other variables' observations in general (saying 80,000 compared to 100,000).
But now, when reflecting back, I am wondering if there is any reference or explanation for excluding action like that (excluding a covariate having many missing observation)? I think it may relate to the within-sample standard variation and explanation power due to the sample shrinking but I am not sure about that.
