Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In this paper, we extend Jradi et al. (2019). First, we use the asymmetric Laplace distribution which is a more reasonable assumption in quantile models. Second, we address the issue of statistical inference for the optimal quantile. Finally, we allow for endogeneity in quantile stochastic frontier models. The new formulation is implemented in a Bayesian framework using Markov Chain Monte Carlo. We employ the celebrated Philippine rice data as in Jradi et al. (2019). Jradi et al. (2019) did not provide efficiency measures which, in our framework, is straightforward to do.