Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of $\sqrt n$ -consistent nonlinear estimators whose cardinality increases with sample size. The method can be compared with the usual approaches of combining the moment conditions (GMM) and combining the instruments (IV), and achieves similar objectives of aggregating the available information. One advantage of aggregating the estimators rather than the moment conditions is that it yields robustness to certain types of parameter heterogeneity in the sense that it delivers consistent estimates of the mean effect in that case. We discuss the question of optimal weighting of the estimators.