Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
One of the most popular estimators of interactive effects panel data models is the common correlated effects (CCE) approach, which uses the cross-sectional averages of the observables to estimate the unobserved factors. The present paper proposes a simple test statistic that is suitable for testing hypotheses about these factors. The statistic can be used to test if a subset of the averages is enough to estimate the factors, or if there are observable variables that capture them. The statistic can also be used sequentially to determine the smallest set of averages needed to estimate the factors.