What is the advantage of having balanced panel data rather than unbalanced? I studied the standard econometrics textbooks about panel data, but most textbooks only mention the difference between balanced and unbalanced panels. The advantage of having balanced panel data is not usually explained. I would like to know: What is the advantage of having a balanced panel? I believe unbalanced panels are much more common in real research. 
 A: I think whenever you have unbalanced panels, you need to come up with a formal description of why that is the case. You need to worry about self-selection, nonresponse, and attrition, especially if you're interested in population parameters and consistency. For most estimators, the mechanics are largely the same.
A: I believe these are largely historical reasons. In the 1940s, one had to conduct analysis of variance with paper and pencil, so having balanced designs led to simple sums for both means and variances. Any imbalance would require inverting matrices 4x4 or larger (I've done it a couple of times on regression exams, and nearly always screwed up). It is likely that in the 1960s when panel/longitudinal data first came to researchers' attention (probably with PSID), one could reasonably easily run a regression with no structure on errors already, but running GLS required heroic efforts, let alone unbalanced GLS. These days, there aren't any issues, as Dimitriy said, as all estimators are computed in the general form with the most general matrix inversion operations in the background, anyway.
Also, with balanced data sets, you can easily run models with panel autoregressions. With unbalanced panels, these will likely get trickier. I don't think that these models are actually that popular.
A: Balanced data is preferred over unbalanced panels, because it allows an observation of the same unit (e.g., individual, company, person, etc.) in every time period (e.g., year, month, etc.), which reduces the noise introduced by unit (individual, etc.) heterogeneity.
