Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We propose a fairly general individual effects stochastic frontier model, which allows both heterogeneity and inefficiency to change over time. Moreover, our model handles the endogeneity problems if either at least one of the regressors or one-sided error term is correlated with the two-sided error term. Our Monte Carlo experiments show that our estimator performs well. We employed our methodology to the US banking data and found a negative relationship between return on revenue and cost efficiency. Estimators ignoring time-varying heterogeneity or endogeneity did not perform well and gave very different estimates compared to our estimator.