Of Nickell Bias and its Cures: Comment on Gaibulloev, Sandler, and Sul
Gaibulloev, Sandler, and Sul (2014) (here after GSS) present two methodological suggestions for estimating dynamic panel models with fixed effects and provide an empirical application using them. Our interest is only in their methodological suggestions, so we do not discuss the empirical application here. One of their methodological suggestions is that analysts account for cross-sectional dependence by adjoining to the model a common factor which relates to events going on in the world that are not explained by the unit-level covariates. This is surely an interesting way to proceed, though we await further evidence on whether the recommended method is superior to standard spatial econometric approach. Since GSS do not discuss this comparison, we do not either, but their approach is clearly of potential interest. The second suggestion, that there is a problem with Nickell (1981) bias when the number of cross-sectional units (N) is considerably greater than the number of time points (T), and that this problem can be solved by simply analyzing subsets of the units independently, is on its face puzzling. In fact, we will argue that it is misguided. This suggestion is puzzling because usually in statistical analysis more data are better than less data. GSS suggest that less data, or equivalently, independent analyses of subsets of the data, is superior to using all the data simultaneously. As with any suggested fix for a methodological problem, we ask: (1) does the problem exist; (2) is the problem serious in applied work; and (3) does the proposed solution do more good than harm? The answer to the first question is yes, but the answers to the last two questions are clearly no.