So far we have dealt only with “complete panels” or “balanced panels,” i.e., cases where the individuals are observed over the entire sample period. Incomplete panels are more likely to be the norm in typical economic empirical settings. For example, in collecting data on US airlines over time, a researcher may find that some firms have dropped out of the market while new entrants emerged over the sample period observed. Similarly, while using labor or consumer panels on households, one may find that some households moved and can no longer be included in the panel. Additionally, if one is collecting data on a set of countries over time, a researcher may find that some countries can be traced back longer than others. These typical scenarios lead to “unbalanced” or “incomplete” panels. To simplify the presentation, we analyze the case of two cross sections with an unequal number of time-series observations and then generalize the analysis to the case of N cross sections. Let \(n_{1}\) be the shorter time series observed for the first cross section \((i=1)\) , and \(n_{2}\) be the extra time-series observations available for the second cross section \((i=2)\) .